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Relationship between internet addiction and depression among Japanese university students
Journal of Affective Disorders 256 (2019) 668–672
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Relationship between internet addiction and depression among Japanese university students
T
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Tomokazu Seki, Kei Hamazaki, Takashi Natori, Hidekuni Inadera
Department of Public Health, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194 Japan
A R T I C LE I N FO
A B S T R A C T
Keywords: Depression Internet addiction Mobile phone use University students
Background: Internet addiction (IA) has various adverse effects. We sought to elucidate the relationship between IA and depression among university students and to identify factors associated with IA. Methods: Anonymous, self-administered questionnaires were distributed to 5,261 students and comprised basic characteristics, lifestyle habits, anxieties, the Internet Addiction Test (IAT), and the Center for Epidemiological Self-Depression Scale. Results: Responses were obtained from 4,490 students (response rate: 85.3%). After excluding those with missing responses, 3,251 participants were analyzed (valid response rate: 61.8%). Logistic regression analysis with severity of IA as the independent variable and depression as the dependent variable revealed that the odds ratio (OR) for depression increased with severity of IA (mild addiction: OR=2.87, 95% confidence interval [CI] =2.45−3.36; severe addiction: OR=7.31, 95% CI=4.61−11.61). In a logistic regression analysis with mobile phone use as the independent variable and IA as the dependent variable, the highest OR was for message board use (OR=3.74, 95% CI=2.53−5.53) and the lowest OR was for use of LINE instant messenger (OR=0.59, 95% CI=0.49−0.70). Logistic regression analysis with academic department as the independent variable and internet addiction as the dependent variable revealed high ORs for the humanities department (OR=1.59, 95% CI=1.18−2.16) and fine arts department (OR=1.55, 95% CI=1.07−2.23). Limitations: The main limitations were the cross-sectional design, low valid response rate, single-university setting, and possible social desirability bias. Conclusions: Our results suggest a relationship between IA and depression in university students. IA tendency differed according to mobile phone use and academic department, suggesting these factors are associated with IA.
1. Introduction The internet began to expand on a global scale in the 1990s and has profoundly changed our lives. The rapid proliferation of smartphones beginning in the 2010s has made internet usage even more commonplace. In Japan, the penetration rate of smartphones has exceeded 80%, with the number of smartphone users exceeding 100 million. According to Japan's Ministry of Internal Affairs and Communications (MIC, 2015), more than 95% of Japanese people aged 13−49 years use smartphones, primarily for e-mail, social media, and shopping. According to previous studies, internet-literate students with a strong command of the internet perform well academically (Leung and Lee, 2012; Siraj, 2015). It is now safe to say that the internet is a fundamental part of daily life.
The internet also has a number of negative effects, however. We have seen the emergence of internet addiction, which is characterized by that an inability to control excessive internet use, and results in moderate to severe problems in daily life. The impact of such addiction is similar to that of alcoholism or compulsive gambling (Young, 1996). Beginning with findings of an association between internet addiction and depression (Young and Rodgers, 1998), the spread of the internet has been reported by many researchers worldwide to have led to a relationship between internet addiction and mental health problems such as depression, anxiety, stress, and reduced happiness (Akin, 2012; Nassehi et al., 2017; Othman and Lee, 2017; Tran et al., 2017; Uddin et al., 2016). Young people born in the internet age, so-called digital natives, are considered to be particularly susceptible to internet addiction due to being brought up in an environment where smartphones
Abbreviations: CES-D, Center for Epidemiological Self-Depression Scale; CI, confidence interval; IAT, Internet Addiction Test; OR, odds ratio ⁎ Corresponding author. E-mail address: [email protected] (H. Inadera). https://doi.org/10.1016/j.jad.2019.06.055 Received 26 April 2019; Received in revised form 24 June 2019; Accepted 30 June 2019 Available online 02 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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divide participants into a group of those with depressive symptoms (≥ 16) and a group of those without depressive symptoms (≤ 15). The CES-D is a highly regarded measure of depression and its validity and reliability have been demonstrated (Radloff, 1991; Umegaki and Todo, 2017). We excluded participants with missing responses for IAT score, CESD score, age, sex, year in university, anxieties (relationships with friends, finances, or academics), gaming habit, alcohol consumption, or smoking. Our analysis ultimately included 3251 participants (valid response rate: 61.8%). Data are expressed as the mean ± standard deviation (SD) or as the number of participants (%). In descriptive statistics, the Kruskal-Wallis test was used to analyze IAT score, CES-D score, and age among groups with different severity of internet addiction (no addiction, mild addiction, severe addiction). Spearman's χ2 test was used to analyze the relationships of severity of internet addiction with sex, anxieties (relationships with friends, finances, or academics), gaming habit, alcohol consumption, and smoking (number of participants [%]). We also performed three logistic regression analyses to examined whether the odds ratio (OR) for depression would change based on the severity of internet addiction and what factors were associated with internet addiction. In one logistic regression analyses, we used severity of internet addiction (reference: no addiction) as the independent variable, depression as the dependent variable, and age, sex, anxieties, gaming habit, smoking, and alcohol consumption as covariates. In the other two logistic regression analyses, we examined the relationship of internet addiction with mobile phone use and academic department (reference: education department), with internet addiction as the dependent variable and age, sex, anxieties, gaming habit, smoking, and alcohol consumption as covariates. In all tests, the level of statistical significance was defined as α = 0.05. Statistics were analyzed using IBM SPSS Statistics Version 24 (IBM Japan, Tokyo).
and other internet-connected devices are available from an early age (Koo and Kwon, 2014; Kraut et al., 1998; Morrison and Gore, 2010). Many studies on internet addiction among young people have been conducted in Japan. A panel study conducted by Japan's MIC using the Japanese version of the Internet Addiction Test (IAT) found strong internet addiction tendency among 2.3% of elementary school students (ages 9 − 11 years), 7.6% of junior high school students (12−14 years), 9.2% of high school students (15−17 years), and 6.1% of university students (MIC, 2013). Other studies have reported a relationship between internet addiction and mental health problems such as depression and loneliness (Ezoe and Toda, 2013; Takahira et al., 2008), as well as relationships between excessive internet use and sleep habits, disorderly eating habits, and reduced quality of life (Taguchi, 2008; Takahashi et al., 2018). Studies outside of Japan have found a relationship between internet addiction and self-harm behaviors such as suicidal ideation and suicide planning (Lin et al., 2014; Marchant et al., 2017). Depression associated with internet addiction is considered a particularly important factor in triggering suicidal ideation and suicide planning (Fu et al., 2010; Kaess et al., 2014; Lam and Peng, 2010). Despite the above findings, most studies on internet addiction and depression have not included sufficiently large numbers of participants (Kuss et al., 2014). To rectify this problem, we have conducted a questionnaire survey with students across a wide range of academic departments with the aim of elucidating the relationship between internet addiction and depression among young people and identifying factors associated with internet addiction. 2. Methods Participants were 5261 first-year to sixth-year students at a national university in Japan who were enrolled in seven departments (humanities, economics, education, fine arts, science, pharmaceutical sciences, and medicine). In Japan, unlike in the US, medical and pharmacy programs are integrated six-year degree programs. The first two years serve the same purpose as a 4-year degree in the US system, while the last four years are equivalent to a 4-year doctoral program in medicine or pharmacy in the US system. In 2014, we conducted an anonymous, self-administered questionnaire survey comprising basic characteristics, lifestyle habits, anxieties, the IAT, and the Center for Epidemiological Self-Depression Scale (CES-D). Most questionnaires were distributed and collected in lectures, recitations, and seminars during class time. Some students received the questionnaire from departmental offices or professors and submitted them personally at a later date. The questionnaire included a cover sheet with an explanation of the study and a consent form that was intended for subjects to keep. Return of the completed questionnaire was taken as implied consent. This study was approved by the institutional review board of the University of Toyama (No. 25-100 and No. 30-97). Internet addiction was assessed using a Japanese version of the IAT (consisting of 20 items scored on a 5-point scale), which was developed as a measure of the severity of internet addiction. In this study, as in the above-mentioned study conducted by the MIC, a score of ≤ 39 was classified as no addiction, a score of 40−69 was classified as mild addiction, and a score of ≥ 70 was classified as severe addiction. In addition, 40 was set as a cutoff score to divide participants into a group with no internet addiction (≤ 39) and a group with internet addiction (≥ 40) (MIC, 2013). The reliability of the Japanese version of the IAT has been demonstrated, with a Cronbach's α of 0.92. Formal validation of the Japanese version was reported by Osada (2013). Depression was assessed using a Japanese version of the CES-D (consisting of 20 items scored on a 5-point scale), which was originally developed as a measure of depression by Radloff (1977) and later validated in Japanese by Shima et al. (1985). In the present study, as in the study by Shima et al. (1985) 16 was established as a cutoff score to
3. Results Responses were obtained from 4490 participants (response rate: 85.3%). Valid responses were obtained from 3251 participants (valid response rate: 61.8%). As shown in Table 1, mean overall IAT score was 41.5 ± 14.5; mild and severe internet addiction was identified in 43.9% and 4.6% of the university students, respectively. Mean overall CES-D score was 17.1 ± 9.5. Mean CES-D scores for the no addiction, mild addiction, and severe addiction groups were 14.1 ± 8.3, 19.6 ± 9.1, and 26.7 ± 10.9, respectively. The percentage of participants with internet addiction was significantly higher among men than among women (n = 783 (50.6%) vs. n = 793 (46.6%); p < 0.01). The percentage of participants with anxieties (relationships with friends, finances, or academics) and gaming habit increased significantly with increasing severity of internet addiction (p < 0.01). Internet addiction was not significantly associated with age, alcohol consumption, or smoking (p = 0.82, 0.11, and 0.36, respectively). Table 2 shows the odds ratios and 95% confidence intervals for depressive symptoms according to severity of internet addiction. The adjusted ORs in each model showed no significant differences. As shown in Fig. 1, the OR for internet addiction according to mobile phone use was highest for message boards (OR=3.74, 95% confidence interval (CI)=2.53−5.53) and lowest for the instant messaging app LINE (OR=0.59, 95% CI=0.49−0.70). Fig. 2 shows that the OR for internet addiction according to academic department (reference: education department) was highest for the humanities department (OR=1.59, 95% CI=1.18−2.16), followed by the fine arts department (OR=1.55, 95% CI=1.07−2.23) and the economics department (OR=1.51, 95% CI=1.16−1.97).
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Table 1 Characteristics of participants according to severity of internet addiction.
IAT score, mean ± SD CES-D score, mean ± SD Age, mean ± SD Men, n (%) Anxiety about relationships with friends, n (%) Anxiety about finances, n (%) Anxiety about academics, n (%) Gaming habit, n (%) Alcohol consumption, n (%) Smoking, n (%)
No addiction (n = 1675)
Mild addiction (n = 1426)
Severe addiction (n = 150)
Overall (N = 3251)
P value
29.9 ± 5.5 14.1 ± 8.3 20.9 ± 2.2 765 (45.7) 747 (44.6) 353 (22.1) 625 (37.3) 249 (14.9) 1319 (78.7) 125 (7.5)
51.0 ± 7.9 19.6 ± 9.1 20.9 ± 2.1 700 (49.1) 846 (59.3) 442 (31.0) 752 (52.7) 322 (22.6) 1134 (79.5) 105 (7.4)
78.9 ± 8.3 26.7 ± 10.9, 20.9 ± 2.1 83 (55.3) 105 (70.0) 70 (46.7) 79 (52.7) 47 (31.3) 113 (75.3) 6 (4.0)
41.5 ± 14.8 17.1 ± 9.5 20.9 ± 2.2 1548 (47.6) 1698 (52.2) 865(26.6) 1456 (44.8) 618 (19.0) 2556 (78.6) 236 (7.3)
<0.01a <0.01a 0.82a 0.025b <0.01b <0.01b <0.01b <0.01b 0.11b 0.36b
IAT: Internet Addiction Test; SD: standard deviation. a Kruskal–Wallis test. b Spearman's χ2 test.
university students in Iran (Bahrainian et al., 2014), and 2.0% among junior high school students in Japan (Kawabe et al., 2016). Compared with these results, the prevalence of severe internet addiction among Japanese university students in this study is high. In terms of the relationship between internet addiction and depression, some previous studies have reported that people with depression or other mental health problems are susceptible to addiction to gaming and mobile phones (Choo et al., 2010; Elhai et al., 2017), while other studies have reported that depression is a major factor in susceptibility to internet addiction (Kuss et al., 2014; Young and Rodgers, 1998). A study in which pharmacotherapy was given to participants with depression and internet addiction found that internet addiction improved (Kuss and Lopez-Fernandez, 2016). However, longitudinal studies on internet addiction have noted that participants with high scores on internet addiction measures are prone to develop more severe internet addiction and also to have mental health problems such as depression (Ko et al., 2014; Lau et al., 2017; Strittmatter et al., 2016). Continued use of the internet for health purposes is also reported to be associated with more severe depression (Bessiere et al., 2010). In addition, internet addiction is significantly associated with dissociation and other forms of reduced sociality (Canan et al., 2012). The above studies suggest a vicious cycle in which depression and internet addiction enhance each other as follows: people rely on the internet to relieve daily stress, causing them to use the internet excessively, thus causing them to be less social; the resulting deterioration of interpersonal relationships in the real world and on the internet causes further stress, which exacerbates depression, which people try to relieve by burying themselves in the internet. Male sex is a reported risk factor for internet addiction (Ostovar et al., 2016; Tsai et al., 2009). We also found that the percentage of participants with an internet addiction was significantly higher among men than among women. Previous studies have indicated that men are
Table 2 Odds ratio and 95% confidence intervals of depressive symptoms according to severity of internet addiction.
No (%) Model 1 Model 2 Model 3
No addiction
Mild addiction
Severe addiction
1675 (51.5) 1.00 1.00 1.00
1426 (43.9) 3.17 (2.74–3.68) 3.21 (2.77–3.72) 2.87 (2.45–3.36)
150 (4.6) 9.08 (5.84–14.11) 9.33 (6.00–14.52) 7.31 (4.61–11.61)
Model 1: No adjustment; Model 2: Adjustment for age and sex; Model 3: Model 2 + Adjustment for anxieties, gaming habit, smoking, and alcohol consumption.
4. Discussion The results of this study suggest that as internet addiction becomes more severe, its relationship with depression becomes stronger. In addition, the use of message boards on mobile phones was suggested to be strongly associated with internet addiction. Belonging to the humanities department, the fine arts department, or the economics department was also suggested to be associated with internet addiction. The prevalence of severe internet addiction in this study was slightly lower than the figure of 6.1% reported among university students by the MIC (2013). This disparity is possibly due to overestimation of internet addiction in the MIC study, which was a panel study of internet users. As indicated by Kuss et al. (2014), there is no gold standard measure of internet addiction. Consequently, the prevalence of internet addiction varies greatly among the plethora of addiction measures used and prevalence rates are generally not comparable across studies. Previous studies that used an IAT score of 70 as a cutoff for severe internet addiction have reported internet addiction rates of 2.5% among high school students in South Korea (Choi et al., 2009), 2.2% among
Fig. 1. Odds ratios of internet addiction according to mobile phone/smartphone use among university students. 670
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Fig. 2. Odds ratio of internet addiction according to university department (reference: education department).
medicine spend relatively long periods of time studying outside of classes (writing lab reports, etc.), thus reducing their internet use and consequently reducing their susceptibility to internet addiction. In order for students in liberal arts programs to increase engagement in their studies and avoid the temptation of excessive internet use, we suggest that universities take steps to make their educational programs more rigorous and practical. For example, because humanities departments include a number of fields of research, such as linguistics, anthropology, and historical science, it would be reasonable to allocate more of a student's time to research, academic writing, gaining qualifications, or attending symposia.
more susceptible to internet addiction than women because men are more likely to play video games (Kawabe et al., 2016; Ko et al., 2005). In addition, internet gaming is considered a risk factor for internet addiction (Kuss et al., 2013; Morrison and Gore, 2010). The above suggests that sex differences in internet addiction may be affected by sex differences in the use of addictive content. In addition to internet gaming, social networking is also considered a risk factor for internet addiction (Kuss et al., 2013). The desire to maintain connections formed on social media may lead to excessive social networking (Griffiths, 2013). In the present study, message board use was suggested to pose a particular risk of internet addiction, whereas the use of LINE was suggested to potentially reduce the risk of internet addiction. A previous study found that its internet addiction group included a significantly high percentage of participants who used chat/community websites for long periods (Morrison and Gore, 2010). Another study reported that online communication with friends and family reduces depression, meaning that the internet can help to strengthen social connections (Bessiere et al., 2010). Together, these findings suggest that the anonymity and interactions with large numbers of people involved in message boards can easily degrade online relationships (e.g., due to online abuse), and attempts to relieve the resulting stress lead to further addiction. In contrast, LINE involves communication with friends and family, which is surmised to relieve stress and yield other positive effects. A 2016 survey on university students’ learning conducted by the National Institute for Educational Policy Research (NIER) compared weekly study time by major among first-year and second-year students, who spend long periods of time in classes. Students in STEM fields (science, technology, engineering, and mathematics) were found to spend relatively long periods of time in classes as well as on preparation and review. In contrast, students in liberal arts (fine arts, humanities, etc.) were found to spend relatively little time in class, or on preparation or review (NIER, 2016). In addition, previous studies have reported that boredom (i.e. excessive free time) leads to internet use and is therefore a major risk factor in internet addiction (Biolcati et al., 2018; Wegmann et al., 2018). In light of the above findings, the results of the present study suggest that students in the economics department and the humanities department have relatively large amounts of free time, which tend to be spent on internet gaming and social media, which increase susceptibility to internet addiction. In contrast, it is suggested that students in the education department and the department of
5. Limitations Firstly, because this study had a cross-sectional design, we cannot determine any cause or effect regarding the results. Secondly, the study was conducted at a single university, so the participants may not be representative of the population of Japanese university students overall. Thirdly, the low valid response rate of approximately 50% indicates possible selection bias. Fourthly, because this study involved a questionnaire survey, respondents may have underreported information that would cast them in a bad light. Lastly, because the online services available to us change greatly every year, the results may not sufficiently reflect the current internet environment. 6. Conclusion The results of this study suggest a relationship between internet addiction and depression among Japanese university students. Our results also indicate that internet addiction is associated with mobile phone use and academic department. In the future, following the same group of participants over time may lead to a deeper understanding of these results. CRediT authorship contribution statement Tomokazu Seki: Conceptualization, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Kei Hamazaki: Conceptualization, Data curation, Formal analysis, Writing - review & editing. Takashi Natori: Conceptualization, Writing - review & editing. Hidekuni Inadera: Data curation, Funding acquisition, Writing 671
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review & editing.
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Acknowledgements We are grateful to Ms. Nozomi Kawaguchi, Ms. Fumie Ohno, Dr. Mika Kigawa, Dr. Zheng-Guo Cui, and Dr. Ryoko Morozumi of the University of Toyama for technical assistance. Conflict of interest All authors declare that there are no conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. Funding This work was supported by intramural grants-in-aid for scientific research. The funding sources had no role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the article for publication. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2019.06.055. References Akin, A., 2012. The relationships between internet addiction, subjective vitality, and subjective happiness. Cyberpsychol. Behav. Soc. Netw. 15, 404–410. Bahrainian, S.A., Alizadeh, K.H., Raeisoon, M.R., Gorji, O.H., Khazaee, A., 2014. Relationship of internet addiction with self-esteem and depression in university students. J. Prev. Med. Hyg. 55, 86–89. Bessiere, K., Pressman, S., Kiesler, S., Kraut, R., 2010. Effects of internet use on health and depression: a longitudinal study. J. Med. Internet Res. 12, e6. Biolcati, R., Mancini, G., Trombini, E., 2018. Proneness to boredom and risk behaviors during Adolescents' free time. Psychol. Rep. 121, 303–323. Canan, F., Ataoglu, A., Ozcetin, A., Icmeli, C., 2012. The association between internet addiction and dissociation among Turkish college students. Compr. Psychiatry 53, 422–426. Choi, K., Son, H., Park, M., Han, J., Kim, K., Lee, B., Gwak, H., 2009. Internet overuse and excessive daytime sleepiness in adolescents. Psychiatry Clin. Neurosci. 63, 455–462. Choo, H., Gentile, D.A., Sim, T., Li, D., Khoo, A., Liau, A.K., 2010. Pathological videogaming among Singaporean youth. Ann. Acad. Med. Singap. 39, 822–829. Elhai, J.D., Dvorak, R.D., Levine, J.C., Hall, B.J., 2017. Problematic smartphone use: a conceptual overview and systematic review of relations with anxiety and depression psychopathology. J. Affect. Disord. 207, 251–259. Ezoe, S., Toda, M., 2013. Relationships of loneliness and mobile phone dependence with internet addiction in Japanese medical students. Open. J. Prev. Med. 3, 407–412. Fu, K.W., Chan, W.S., Wong, P.W., Yip, P.S., 2010. Internet addiction: prevalence, discriminant validity and correlates among adolescents in Hong Kong. Br. J. Psychiatry 196, 486–492. Griffiths, M.D., 2013. Social networking Addiction: emerging themes and issues. J. Addict. Res. Ther. 4. Kaess, M., Durkee, T., Brunner, R., Carli, V., Parzer, P., Wasserman, C., Sarchiapone, M., Hoven, C., Apter, A., Balazs, J., Balint, M., Bobes, J., Cohen, R., Cosman, D., Cotter, P., Fischer, G., Floderus, B., Iosue, M., Haring, C., Kahn, J.P., Musa, G.J., Nemes, B., Postuvan, V., Resch, F., Saiz, P.A., Sisask, M., Snir, A., Varnik, A., Ziberna, J., Wasserman, D., 2014. Pathological internet use among european adolescents: psychopathology and self-destructive behaviours. Eur. Child. Adolesc. Psychiatry 23, 1093–1102. Kawabe, K., Horiuchi, F., Ochi, M., Oka, Y., Ueno, S., 2016. Internet addiction: prevalence and relation with mental states in adolescents. Psychiatry Clin. Neurosci. 70, 405–412. Ko, C.H., Liu, T.L., Wang, P.W., Chen, C.S., Yen, C.F., Yen, J.Y., 2014. The exacerbation of depression, hostility, and social anxiety in the course of internet addiction among adolescents: a prospective study. Compr. Psychiatry 55, 1377–1384. Ko, C.H., Yen, J.Y., Chen, C.C., Chen, S.H., Yen, C.F., 2005. Gender differences and related factors affecting online gaming addiction among taiwanese adolescents. J. Nerv. Ment. Dis. 193, 273–277. Koo, H.J., Kwon, J.H., 2014. Risk and protective factors of internet addiction: a metaanalysis of empirical studies in Korea. Yonsei Med. J. 55, 1691–1711. Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukopadhyay, T., Scherlis, W., 1998. Internet paradox. A social technology that reduces social involvement and psychological well-being? Am. Psychol. 53, 1017–1031. Kuss, D.J., Griffiths, M.D., Karila, L., Billieux, J., 2014. Internet addiction: a systematic review of epidemiological research for the last decade. Curr. Pharm. Des. 20,
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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad
Research paper
Relationship between internet addiction and depression among Japanese university students
T
⁎
Tomokazu Seki, Kei Hamazaki, Takashi Natori, Hidekuni Inadera
Department of Public Health, Faculty of Medicine, University of Toyama, 2630 Sugitani, Toyama, 930-0194 Japan
A R T I C LE I N FO
A B S T R A C T
Keywords: Depression Internet addiction Mobile phone use University students
Background: Internet addiction (IA) has various adverse effects. We sought to elucidate the relationship between IA and depression among university students and to identify factors associated with IA. Methods: Anonymous, self-administered questionnaires were distributed to 5,261 students and comprised basic characteristics, lifestyle habits, anxieties, the Internet Addiction Test (IAT), and the Center for Epidemiological Self-Depression Scale. Results: Responses were obtained from 4,490 students (response rate: 85.3%). After excluding those with missing responses, 3,251 participants were analyzed (valid response rate: 61.8%). Logistic regression analysis with severity of IA as the independent variable and depression as the dependent variable revealed that the odds ratio (OR) for depression increased with severity of IA (mild addiction: OR=2.87, 95% confidence interval [CI] =2.45−3.36; severe addiction: OR=7.31, 95% CI=4.61−11.61). In a logistic regression analysis with mobile phone use as the independent variable and IA as the dependent variable, the highest OR was for message board use (OR=3.74, 95% CI=2.53−5.53) and the lowest OR was for use of LINE instant messenger (OR=0.59, 95% CI=0.49−0.70). Logistic regression analysis with academic department as the independent variable and internet addiction as the dependent variable revealed high ORs for the humanities department (OR=1.59, 95% CI=1.18−2.16) and fine arts department (OR=1.55, 95% CI=1.07−2.23). Limitations: The main limitations were the cross-sectional design, low valid response rate, single-university setting, and possible social desirability bias. Conclusions: Our results suggest a relationship between IA and depression in university students. IA tendency differed according to mobile phone use and academic department, suggesting these factors are associated with IA.
1. Introduction The internet began to expand on a global scale in the 1990s and has profoundly changed our lives. The rapid proliferation of smartphones beginning in the 2010s has made internet usage even more commonplace. In Japan, the penetration rate of smartphones has exceeded 80%, with the number of smartphone users exceeding 100 million. According to Japan's Ministry of Internal Affairs and Communications (MIC, 2015), more than 95% of Japanese people aged 13−49 years use smartphones, primarily for e-mail, social media, and shopping. According to previous studies, internet-literate students with a strong command of the internet perform well academically (Leung and Lee, 2012; Siraj, 2015). It is now safe to say that the internet is a fundamental part of daily life.
The internet also has a number of negative effects, however. We have seen the emergence of internet addiction, which is characterized by that an inability to control excessive internet use, and results in moderate to severe problems in daily life. The impact of such addiction is similar to that of alcoholism or compulsive gambling (Young, 1996). Beginning with findings of an association between internet addiction and depression (Young and Rodgers, 1998), the spread of the internet has been reported by many researchers worldwide to have led to a relationship between internet addiction and mental health problems such as depression, anxiety, stress, and reduced happiness (Akin, 2012; Nassehi et al., 2017; Othman and Lee, 2017; Tran et al., 2017; Uddin et al., 2016). Young people born in the internet age, so-called digital natives, are considered to be particularly susceptible to internet addiction due to being brought up in an environment where smartphones
Abbreviations: CES-D, Center for Epidemiological Self-Depression Scale; CI, confidence interval; IAT, Internet Addiction Test; OR, odds ratio ⁎ Corresponding author. E-mail address: [email protected] (H. Inadera). https://doi.org/10.1016/j.jad.2019.06.055 Received 26 April 2019; Received in revised form 24 June 2019; Accepted 30 June 2019 Available online 02 July 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.
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divide participants into a group of those with depressive symptoms (≥ 16) and a group of those without depressive symptoms (≤ 15). The CES-D is a highly regarded measure of depression and its validity and reliability have been demonstrated (Radloff, 1991; Umegaki and Todo, 2017). We excluded participants with missing responses for IAT score, CESD score, age, sex, year in university, anxieties (relationships with friends, finances, or academics), gaming habit, alcohol consumption, or smoking. Our analysis ultimately included 3251 participants (valid response rate: 61.8%). Data are expressed as the mean ± standard deviation (SD) or as the number of participants (%). In descriptive statistics, the Kruskal-Wallis test was used to analyze IAT score, CES-D score, and age among groups with different severity of internet addiction (no addiction, mild addiction, severe addiction). Spearman's χ2 test was used to analyze the relationships of severity of internet addiction with sex, anxieties (relationships with friends, finances, or academics), gaming habit, alcohol consumption, and smoking (number of participants [%]). We also performed three logistic regression analyses to examined whether the odds ratio (OR) for depression would change based on the severity of internet addiction and what factors were associated with internet addiction. In one logistic regression analyses, we used severity of internet addiction (reference: no addiction) as the independent variable, depression as the dependent variable, and age, sex, anxieties, gaming habit, smoking, and alcohol consumption as covariates. In the other two logistic regression analyses, we examined the relationship of internet addiction with mobile phone use and academic department (reference: education department), with internet addiction as the dependent variable and age, sex, anxieties, gaming habit, smoking, and alcohol consumption as covariates. In all tests, the level of statistical significance was defined as α = 0.05. Statistics were analyzed using IBM SPSS Statistics Version 24 (IBM Japan, Tokyo).
and other internet-connected devices are available from an early age (Koo and Kwon, 2014; Kraut et al., 1998; Morrison and Gore, 2010). Many studies on internet addiction among young people have been conducted in Japan. A panel study conducted by Japan's MIC using the Japanese version of the Internet Addiction Test (IAT) found strong internet addiction tendency among 2.3% of elementary school students (ages 9 − 11 years), 7.6% of junior high school students (12−14 years), 9.2% of high school students (15−17 years), and 6.1% of university students (MIC, 2013). Other studies have reported a relationship between internet addiction and mental health problems such as depression and loneliness (Ezoe and Toda, 2013; Takahira et al., 2008), as well as relationships between excessive internet use and sleep habits, disorderly eating habits, and reduced quality of life (Taguchi, 2008; Takahashi et al., 2018). Studies outside of Japan have found a relationship between internet addiction and self-harm behaviors such as suicidal ideation and suicide planning (Lin et al., 2014; Marchant et al., 2017). Depression associated with internet addiction is considered a particularly important factor in triggering suicidal ideation and suicide planning (Fu et al., 2010; Kaess et al., 2014; Lam and Peng, 2010). Despite the above findings, most studies on internet addiction and depression have not included sufficiently large numbers of participants (Kuss et al., 2014). To rectify this problem, we have conducted a questionnaire survey with students across a wide range of academic departments with the aim of elucidating the relationship between internet addiction and depression among young people and identifying factors associated with internet addiction. 2. Methods Participants were 5261 first-year to sixth-year students at a national university in Japan who were enrolled in seven departments (humanities, economics, education, fine arts, science, pharmaceutical sciences, and medicine). In Japan, unlike in the US, medical and pharmacy programs are integrated six-year degree programs. The first two years serve the same purpose as a 4-year degree in the US system, while the last four years are equivalent to a 4-year doctoral program in medicine or pharmacy in the US system. In 2014, we conducted an anonymous, self-administered questionnaire survey comprising basic characteristics, lifestyle habits, anxieties, the IAT, and the Center for Epidemiological Self-Depression Scale (CES-D). Most questionnaires were distributed and collected in lectures, recitations, and seminars during class time. Some students received the questionnaire from departmental offices or professors and submitted them personally at a later date. The questionnaire included a cover sheet with an explanation of the study and a consent form that was intended for subjects to keep. Return of the completed questionnaire was taken as implied consent. This study was approved by the institutional review board of the University of Toyama (No. 25-100 and No. 30-97). Internet addiction was assessed using a Japanese version of the IAT (consisting of 20 items scored on a 5-point scale), which was developed as a measure of the severity of internet addiction. In this study, as in the above-mentioned study conducted by the MIC, a score of ≤ 39 was classified as no addiction, a score of 40−69 was classified as mild addiction, and a score of ≥ 70 was classified as severe addiction. In addition, 40 was set as a cutoff score to divide participants into a group with no internet addiction (≤ 39) and a group with internet addiction (≥ 40) (MIC, 2013). The reliability of the Japanese version of the IAT has been demonstrated, with a Cronbach's α of 0.92. Formal validation of the Japanese version was reported by Osada (2013). Depression was assessed using a Japanese version of the CES-D (consisting of 20 items scored on a 5-point scale), which was originally developed as a measure of depression by Radloff (1977) and later validated in Japanese by Shima et al. (1985). In the present study, as in the study by Shima et al. (1985) 16 was established as a cutoff score to
3. Results Responses were obtained from 4490 participants (response rate: 85.3%). Valid responses were obtained from 3251 participants (valid response rate: 61.8%). As shown in Table 1, mean overall IAT score was 41.5 ± 14.5; mild and severe internet addiction was identified in 43.9% and 4.6% of the university students, respectively. Mean overall CES-D score was 17.1 ± 9.5. Mean CES-D scores for the no addiction, mild addiction, and severe addiction groups were 14.1 ± 8.3, 19.6 ± 9.1, and 26.7 ± 10.9, respectively. The percentage of participants with internet addiction was significantly higher among men than among women (n = 783 (50.6%) vs. n = 793 (46.6%); p < 0.01). The percentage of participants with anxieties (relationships with friends, finances, or academics) and gaming habit increased significantly with increasing severity of internet addiction (p < 0.01). Internet addiction was not significantly associated with age, alcohol consumption, or smoking (p = 0.82, 0.11, and 0.36, respectively). Table 2 shows the odds ratios and 95% confidence intervals for depressive symptoms according to severity of internet addiction. The adjusted ORs in each model showed no significant differences. As shown in Fig. 1, the OR for internet addiction according to mobile phone use was highest for message boards (OR=3.74, 95% confidence interval (CI)=2.53−5.53) and lowest for the instant messaging app LINE (OR=0.59, 95% CI=0.49−0.70). Fig. 2 shows that the OR for internet addiction according to academic department (reference: education department) was highest for the humanities department (OR=1.59, 95% CI=1.18−2.16), followed by the fine arts department (OR=1.55, 95% CI=1.07−2.23) and the economics department (OR=1.51, 95% CI=1.16−1.97).
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Table 1 Characteristics of participants according to severity of internet addiction.
IAT score, mean ± SD CES-D score, mean ± SD Age, mean ± SD Men, n (%) Anxiety about relationships with friends, n (%) Anxiety about finances, n (%) Anxiety about academics, n (%) Gaming habit, n (%) Alcohol consumption, n (%) Smoking, n (%)
No addiction (n = 1675)
Mild addiction (n = 1426)
Severe addiction (n = 150)
Overall (N = 3251)
P value
29.9 ± 5.5 14.1 ± 8.3 20.9 ± 2.2 765 (45.7) 747 (44.6) 353 (22.1) 625 (37.3) 249 (14.9) 1319 (78.7) 125 (7.5)
51.0 ± 7.9 19.6 ± 9.1 20.9 ± 2.1 700 (49.1) 846 (59.3) 442 (31.0) 752 (52.7) 322 (22.6) 1134 (79.5) 105 (7.4)
78.9 ± 8.3 26.7 ± 10.9, 20.9 ± 2.1 83 (55.3) 105 (70.0) 70 (46.7) 79 (52.7) 47 (31.3) 113 (75.3) 6 (4.0)
41.5 ± 14.8 17.1 ± 9.5 20.9 ± 2.2 1548 (47.6) 1698 (52.2) 865(26.6) 1456 (44.8) 618 (19.0) 2556 (78.6) 236 (7.3)
<0.01a <0.01a 0.82a 0.025b <0.01b <0.01b <0.01b <0.01b 0.11b 0.36b
IAT: Internet Addiction Test; SD: standard deviation. a Kruskal–Wallis test. b Spearman's χ2 test.
university students in Iran (Bahrainian et al., 2014), and 2.0% among junior high school students in Japan (Kawabe et al., 2016). Compared with these results, the prevalence of severe internet addiction among Japanese university students in this study is high. In terms of the relationship between internet addiction and depression, some previous studies have reported that people with depression or other mental health problems are susceptible to addiction to gaming and mobile phones (Choo et al., 2010; Elhai et al., 2017), while other studies have reported that depression is a major factor in susceptibility to internet addiction (Kuss et al., 2014; Young and Rodgers, 1998). A study in which pharmacotherapy was given to participants with depression and internet addiction found that internet addiction improved (Kuss and Lopez-Fernandez, 2016). However, longitudinal studies on internet addiction have noted that participants with high scores on internet addiction measures are prone to develop more severe internet addiction and also to have mental health problems such as depression (Ko et al., 2014; Lau et al., 2017; Strittmatter et al., 2016). Continued use of the internet for health purposes is also reported to be associated with more severe depression (Bessiere et al., 2010). In addition, internet addiction is significantly associated with dissociation and other forms of reduced sociality (Canan et al., 2012). The above studies suggest a vicious cycle in which depression and internet addiction enhance each other as follows: people rely on the internet to relieve daily stress, causing them to use the internet excessively, thus causing them to be less social; the resulting deterioration of interpersonal relationships in the real world and on the internet causes further stress, which exacerbates depression, which people try to relieve by burying themselves in the internet. Male sex is a reported risk factor for internet addiction (Ostovar et al., 2016; Tsai et al., 2009). We also found that the percentage of participants with an internet addiction was significantly higher among men than among women. Previous studies have indicated that men are
Table 2 Odds ratio and 95% confidence intervals of depressive symptoms according to severity of internet addiction.
No (%) Model 1 Model 2 Model 3
No addiction
Mild addiction
Severe addiction
1675 (51.5) 1.00 1.00 1.00
1426 (43.9) 3.17 (2.74–3.68) 3.21 (2.77–3.72) 2.87 (2.45–3.36)
150 (4.6) 9.08 (5.84–14.11) 9.33 (6.00–14.52) 7.31 (4.61–11.61)
Model 1: No adjustment; Model 2: Adjustment for age and sex; Model 3: Model 2 + Adjustment for anxieties, gaming habit, smoking, and alcohol consumption.
4. Discussion The results of this study suggest that as internet addiction becomes more severe, its relationship with depression becomes stronger. In addition, the use of message boards on mobile phones was suggested to be strongly associated with internet addiction. Belonging to the humanities department, the fine arts department, or the economics department was also suggested to be associated with internet addiction. The prevalence of severe internet addiction in this study was slightly lower than the figure of 6.1% reported among university students by the MIC (2013). This disparity is possibly due to overestimation of internet addiction in the MIC study, which was a panel study of internet users. As indicated by Kuss et al. (2014), there is no gold standard measure of internet addiction. Consequently, the prevalence of internet addiction varies greatly among the plethora of addiction measures used and prevalence rates are generally not comparable across studies. Previous studies that used an IAT score of 70 as a cutoff for severe internet addiction have reported internet addiction rates of 2.5% among high school students in South Korea (Choi et al., 2009), 2.2% among
Fig. 1. Odds ratios of internet addiction according to mobile phone/smartphone use among university students. 670
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Fig. 2. Odds ratio of internet addiction according to university department (reference: education department).
medicine spend relatively long periods of time studying outside of classes (writing lab reports, etc.), thus reducing their internet use and consequently reducing their susceptibility to internet addiction. In order for students in liberal arts programs to increase engagement in their studies and avoid the temptation of excessive internet use, we suggest that universities take steps to make their educational programs more rigorous and practical. For example, because humanities departments include a number of fields of research, such as linguistics, anthropology, and historical science, it would be reasonable to allocate more of a student's time to research, academic writing, gaining qualifications, or attending symposia.
more susceptible to internet addiction than women because men are more likely to play video games (Kawabe et al., 2016; Ko et al., 2005). In addition, internet gaming is considered a risk factor for internet addiction (Kuss et al., 2013; Morrison and Gore, 2010). The above suggests that sex differences in internet addiction may be affected by sex differences in the use of addictive content. In addition to internet gaming, social networking is also considered a risk factor for internet addiction (Kuss et al., 2013). The desire to maintain connections formed on social media may lead to excessive social networking (Griffiths, 2013). In the present study, message board use was suggested to pose a particular risk of internet addiction, whereas the use of LINE was suggested to potentially reduce the risk of internet addiction. A previous study found that its internet addiction group included a significantly high percentage of participants who used chat/community websites for long periods (Morrison and Gore, 2010). Another study reported that online communication with friends and family reduces depression, meaning that the internet can help to strengthen social connections (Bessiere et al., 2010). Together, these findings suggest that the anonymity and interactions with large numbers of people involved in message boards can easily degrade online relationships (e.g., due to online abuse), and attempts to relieve the resulting stress lead to further addiction. In contrast, LINE involves communication with friends and family, which is surmised to relieve stress and yield other positive effects. A 2016 survey on university students’ learning conducted by the National Institute for Educational Policy Research (NIER) compared weekly study time by major among first-year and second-year students, who spend long periods of time in classes. Students in STEM fields (science, technology, engineering, and mathematics) were found to spend relatively long periods of time in classes as well as on preparation and review. In contrast, students in liberal arts (fine arts, humanities, etc.) were found to spend relatively little time in class, or on preparation or review (NIER, 2016). In addition, previous studies have reported that boredom (i.e. excessive free time) leads to internet use and is therefore a major risk factor in internet addiction (Biolcati et al., 2018; Wegmann et al., 2018). In light of the above findings, the results of the present study suggest that students in the economics department and the humanities department have relatively large amounts of free time, which tend to be spent on internet gaming and social media, which increase susceptibility to internet addiction. In contrast, it is suggested that students in the education department and the department of
5. Limitations Firstly, because this study had a cross-sectional design, we cannot determine any cause or effect regarding the results. Secondly, the study was conducted at a single university, so the participants may not be representative of the population of Japanese university students overall. Thirdly, the low valid response rate of approximately 50% indicates possible selection bias. Fourthly, because this study involved a questionnaire survey, respondents may have underreported information that would cast them in a bad light. Lastly, because the online services available to us change greatly every year, the results may not sufficiently reflect the current internet environment. 6. Conclusion The results of this study suggest a relationship between internet addiction and depression among Japanese university students. Our results also indicate that internet addiction is associated with mobile phone use and academic department. In the future, following the same group of participants over time may lead to a deeper understanding of these results. CRediT authorship contribution statement Tomokazu Seki: Conceptualization, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Kei Hamazaki: Conceptualization, Data curation, Formal analysis, Writing - review & editing. Takashi Natori: Conceptualization, Writing - review & editing. Hidekuni Inadera: Data curation, Funding acquisition, Writing 671
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review & editing.
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