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The risk factors of Internet addiction—A survey of university freshmen
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Psychiatry Research 167 (2009) 294 – 299 www.elsevier.com/locate/psychres
The risk factors of Internet addiction—A survey of university freshmen Hsing Fang Tsai a , Shu Hui Cheng a , Tzung Lieh Yeh a,b , Chi-Chen Shih c , Kao Ching Chen a,b , Yi Ching Yang c,⁎, Yen Kuang Yang a,b a
Department of Psychiatry, National Cheng Kung University Hospital, Tainan, Taiwan Department of Psychiatry, College of Medicine, National Cheng Kung University, Tainan, Taiwan Department of Family Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan b
c
Received 29 January 2007; received in revised form 16 January 2008; accepted 25 January 2008
Abstract This study was designed to explore the risk factors of Internet addiction in 1360 freshmen of the National Cheng Kung University in Taiwan in 2003. The test battery included a self-administrated structured questionnaire, the Chinese Internet Addiction Scale-Revision (CIAS-R), the 12-item Chinese Health Questionnaire (CHQ-12), the Measurement of Support Functions (MSF), and the neuroticism subscale of the Maudsley Personality Inventory (MPI). Of the total study population, there were 680 college freshmen (17.9%) in the Internet addiction group, as defined by high CIAS-R scores. Using logistic regression analyses, we found positive relationships between Internet addiction and male gender, neuroticism scores and the CHQ score. In addition, the freshmen who skipped breakfast and those who had poorer social support also had a higher probability of Internet addiction. Internet addiction is prevalent among university freshmen in Taiwan. Risk factors included male gender, habit of skipping breakfast, mental health morbidity, deficient social support; and neurotic personality characteristics. © 2008 Elsevier Ireland Ltd. All rights reserved. Keywords: Internet; Mental health morbidity; Social support function; Neuroticism
1. Introduction The Internet is fast becoming a basic feature of global civilization. Informative, convenient, and entertaining, the Internet has changed the ways people work and spend their leisure time. As of June 2007, 1.133 billion people used the Internet according to the Internet World Stats. However, uncontrolled Internet use may have negative ⁎ Corresponding author. Department of Family Medicine, College of Medicine, National Cheng Kung University, 138, Sheng Li Road, Tainan 70428, Taiwan. Tel.: +886 6 2766165; fax: +886 6 2091433. E-mail address: [email protected] (Y.C. Yang).
impacts on social, occupational, academic, marital and interpersonal adjustment (Baruch, 2001; Parks, 2002; Engelberg and Sjöberg, 2004). Researchers have used various terms to describe individuals who exhibit addictive behaviors in their Internet use, including “computer dependency,” “online dependency,” “cyber addiction,” “pathological Internet use,” and “Internet addiction disorder” (Whang et al., 2003; Lee and Shin, 2004; Song et al., 2004). Addictive use of the Internet was reported by Young (1996), who found that most people with Internet addiction were young males with low sociality and low self-esteem. Scherer (1997) reported that persons who were over-involved with the Internet
0165-1781/$ - see front matter © 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.psychres.2008.01.015
H.F. Tsai et al. / Psychiatry Research 167 (2009) 294–299
exhibited symptoms of Internet addiction and might have an increased risk of psychiatric morbidity. Chen et al. (2003) also reported that people with addictive behaviors were more likely to have health morbidity, socioeconomic problems and behavioral problems. Certain personality traits should be considered when assessing potential mental problems. In a study involving a large sample, Breslau et al. (1991) reported that neuroticism was one of the significant independent predictors of post-traumatic stress disorder (PTSD). In Taiwanese studies, a positive correlation between a personality trait (namely, neuroticism) and psychiatric morbidity has also been reported (Chen et al., 2001; Yang et al., 2003). Therefore, we speculated that neuroticism is a possible risk factor for Internet addiction. Young people are generally believed to constitute the majority of Internet users. An increasing number of studies have revealed that some youngsters are compulsive in their use of the Internet and exhibit addictive behaviors very similar to those related to alcoholism, substance addiction and pathological gambling (Ng and Wiemer-Hastings, 2005; Ha et al., 2006; Petry, 2006). In Johansson and Götestam's (2004) study, 1.98% of Norwegian youth (aged 12–18) were described as having an “Internet addiction” according to the criteria of Young's (1998b) diagnostic questionnaire. However, few studies have explored the correlation between lifestyle habits, mental health and Internet addiction in young people. Compared with other young people, college students may be more involved with the Internet (Kandell, 1998; Young, 2001). The aim of this study was to investigate risk factors associated with Internet addiction among university freshmen. We hypothesized that risk factors related to personality traits, lifestyle habits, interpersonal relationships and the potential of psychiatric morbidity might be elevated in those who are Internet addicted. 2. Methods 2.1. Subjects A total of 4710 freshmen of the National Cheng Kung University were recruited in 2003. Subjects were enrolled during the routine health examination that was part of freshmen orientation. They were invited to participate in this study in which they would complete a self-administered questionnaire about their personal lifestyle habits and online behaviors. Before the study began, informed consent was obtained from all of the study participants. Since they only agreed to have their questionnaire data and related examination results analyzed anonymously,
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any identifying information was kept confidential. The Ethical Committee for Human Research at the National Cheng Kung University Medical Center approved the study protocol. 2.2. Assessment of personal lifestyle habits Through a self-reported questionnaire, the demographic characteristics, personal history, family history, lifestyle habits, and various medical problems were evaluated. The 14-item questionnaire contains questions about the following systemic diseases: hypertension, diabetes mellitus, tuberculosis, asthma, renal disease, polycystic kidney disease, proteinuria, hematuria, urinary stone, arthritis, epilepsy, systemic lupus erythematosus, polio, and hyperlipidemia. The personal lifestyle habits investigated included breakfast skipping, coffee drinking, alcohol drinking and cigarette smoking. Breakfast skipping was defined in our questionnaires as eating breakfast fewer than three times per week. Similarly, coffee drinking was defined as drinking coffee on more than 4 days per week. In addition, former and current alcohol drinkers were assigned to the alcohol group. Similarly, former and current smokers were assigned to the smoking group. As for the amount of smoking, the number of packs of cigarettes smoked per year was calculated. The number of packs of cigarettes smoked per year was determined by multiplying the number of packs (one pack is equal to 20 cigarettes) smoked per day by 365 days per year and by the duration of smoking in years. 2.3. Measurement of psychological symptoms The instruments in this domain included the Chinese Internet Addiction Scale-Revision (CIAS-R) (Chen et al., 2003), the 12-item Chinese Health Questionnaire (CHQ) (Chong and Wilkinson, 1989), the Measurement of Support Functions (MSF) (Lin et al., 1999; Berkman and Glass, 2000), and the 24-item Neuroticism subscale of the Maudsley Personality Inventory (MPI) (Eysenck and Eysenck, 1975). The CIAS-R is a 4-point, 26-item self-rated measure with good reliability and validity (Chen et al., 2003). This test, which has been used to measure the severity of adolescent Internet addiction, includes 26 questions about the core symptoms and the related problems of Internet addiction, and five background questions about basic demographics, weekly online hours, habitual domains, and experience of Internet utilization (Chen et al., 2003). The core symptoms of Internet addiction included tolerance (four questions), compulsive use (five questions)
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and withdrawal (five questions). The related problems included negative impact on their social activities, interpersonal relationships and physical conditions (seven questions) and time management (five questions). The total scores of the CIAS-R range from 26 to 84, with higher CIAS-R scores indicating increased severity of addiction to Internet activity. The cut-off score of 64 or more in the CIAS-R (Ko et al., 2005) was used to identify those who had Internet addiction. This diagnostic cut-off value of 63/64 has been demonstrated to have a high sensitivity rate (86.6%) and an excellent diagnostic accuracy rate (87.6%). This discriminative potential makes the scale a reliable diagnostic tool in an epidemiological survey, as it can provide the estimated prevalence rate and identify the target case group. The CHQ, which is a 12-item questionnaire, can be considered a culturally sensitive tool for detecting potential psychiatric morbidity among Chinese individuals. The CHQ-12 is a standardized self-reported screening instrument. The CHQ-12 has been used in surveys of minor psychiatric morbidity in three communities in Taiwan (Cheng, 1988). It can be used to identify a “probable clinical case” on the basis of a cut-off score. In addition, it can be used to determine the severity of morbidity on the basis of the total score, which ranges from zero to 12. Higher scores indicate more severe psychiatric morbidity. The sensitivity and specificity in predicting cases of psychiatric morbidity were 69.6% and 98.4%, respectively, in a community study (cut-off = 2/3)
(Cheng, 1988), and 78% and 77%, respectively, in general health clinics (cut-off = 3/4) (Chong and Wilkinson, 1989). In our study, a probable case of psychiatric morbidity was defined using a cut-off value of 3/4. A probable case of psychiatric morbidity was defined as having a score of 4 or higher on the CHQ-12 (Yang et al., 2003). The MSF has four subscales, including perceived crisis support (PCS), perceived routine support (PRS), received crisis support (RCS), and received routine support (RRS) (Lin et al., 1999). Perceived versus received support focuses on the subjective versus the objective continuum of support (Turner and Marino, 1994). Perceived support refers to the perception of the availability of support when it is needed, the appraisal of its adequacy, and the quality of such support. Received support refers to the nature and frequency of specific support transactions. The other major dimensions are routine versus crisis supports. Routine support is the process by which support is received or perceived relative to day-to-day activities, whereas crisis support reflects the process by which support is perceived or received when the ego is confronted with a crisis situation or event. In order to further assess the relationship between Internet addiction and support functions, each of these four subscales was further divided into three categories, including low (≤24 points), moderate (25–34 points), and high (≥35 points) scores. The MPI allows simple investigation of neurotic tendencies and deceptive behaviors. In accordance with
Table 1 Differences between the Internet addiction and non-addiction groups. Predictors
Internet non-addiction
Internet addiction
Chi-square test for predictor
N = 680 (%)
N = 680 (%)
χ2
Demographic and health variables Sex (Male) History of systemic disease a (yes) Coffee drinking (yes) Alcohol drinking (yes) Smoking (yes) Breakfast eating (yes)
463 (68.1) 116 (17.1) 89 (13.1) 10 (1.5) 24 (3.5) 382 (56.2)
483 (71.0) 132 (19.4) 65 (9.6) 13 (1.9) 23 (3.4) 281 (41.3)
Assessments b MPI_Neuroticism (≥25) PCS_MSF (low, moderate) RCS_MSF (low, moderate) PRS_MSF (low, moderate) RRS_MSF (low, moderate) CHQ (≥4)
128 (18.7) 28, 546 (4.1, 80.3) 125, 285 (18.4, 41.9) 38, 411 (5.6, 60.4) 38, 411 (5.6, 60.4) 61 (9.0)
315 (46.3) 46, 533 (6.8, 78.4) 149, 323 (21.9, 47.5) 53, 466 (7.8, 68.5) 53, 446 (7.8, 65.6) 140 (20.6)
a
df
P-value
1.39 1.26 4.22 0.40 0.02 30.02
1 1 1 1 1 1
0.23 0.26 0.04 0.52 0.88 b0.0001
118.47 4.66 12.52 9.97 9.97 36.44
1 2 2 2 2 1
b0.0001 0.09 0.002 0.007 0.005 b0.0001
The systemic diseases included hypertension, diabetes mellitus, tuberculosis, asthma, renal disease, polycystic kidney disease, proteinuria, hematuria, urinary stone, arthritis, epilepsy, systemic lupus erythematosus, polio and hyperlipidemia. b MPI: Maudsley Personality Inventory; PCS_MSF: perceived crisis support; RCS_MSF: received crisis support; PRS_MSF: perceived routine support; RRS_MSF: received routine support; CHQ: 12-item Chinese Health Questionnaire.
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MSF, lower support functions of received crisis, perceived routine and received routine were more prevalent in the Internet addiction group. Because sex was a predictor of Internet addiction in some previous studies (Young, 1996), we selected it and other significant variables to enter in the multiple logistic regression model. As shown in Table 2, male gender increased the risk of Internet addiction by 1.3-fold, and breakfast skipping increased that risk by 1.8-fold. Among the various assessments of psychological symptoms, scores on the neuroticism subscale of the MPI, support functions of received crisis, and the CHQ-12 were significant predictors of the risk of Internet addiction.
the criteria of the MPI, subjects scoring 25 points or higher were categorized as abnormal. 2.4. Statistical methods Group differences in categorical variables were analyzed using the Chi-square test. Subsequently, logistic regression was used to analyze the association between the multiple risk factors and Internet addiction. Variables with P b 0.05 in univariate analyses were included in the multivariate models. The two-tailed significance level was set at 0.05. All of the analyses were carried out using the SPSS software (Version 11, SPSS Inc., Chicago, IL, U.S.A.).
4. Discussion 3. Results The prevalence rates of Internet addiction seem to vary with different study populations. Pallanti et al. (2006) reported that 5.4% of Italian high-school students could be characterized as suffering from Internet addiction as defined by the Internet Addiction Scale criteria. Using the Pathological Internet Use Scale, Morahan-Martin and Schumacher (2000) found that 8.1% of U.S. College students had symptoms related to pathological Internet use. In this study, 17.9% of hr Taiwanese university students were found to be Internet addicted. Owing to the differences in target populations, social and cultural contexts, and screening tools, it is difficult to compare these findings directly. Internet addiction was found to be associated with male gender, habit of skipping breakfast, minor mental health morbidity, poor social support function, and neurotic personality characteristics. Some of these findings are consistent with those of other studies on
Questionnaires were completed by 3806 of the participants completed the questionnaires (response rate = 80.81%), and 67.7% of them were male (males = 67.7%). There were 680 (17.9%) freshmen who were classified as possible cases of Internet addiction. Furthermore, in order to analyze the possible risk factors of Internet addiction, these 680 Internet addiction students and 680 randomly selected nonInternet addiction students from the non-Internet addiction group were included. The distinguishing characteristics of the Internet addiction and non-addiction groups are shown in Table 1. Coffee-drinking and breakfast-eating habits were significantly less common among the students with Internet addiction. Of the psychological symptoms, neuroticism and psychiatric morbidity were significantly associated with Internet addiction. Relative to a high score on the
Table 2 Risk factors of Internet addiction based on the logistic regression analyses (N = 1360). Predictors a
df
Demographic and health variables Sex (male vs. female) Breakfast eating (no vs. yes)
1 1
Assessments c MPI_Neuroticism (≧25 vs.b25) RCS_MSF score Low vs. high Moderate vs. high CHQ (≧ 4 vs. b4) Constant
β
P-value
O.R. b
95% C.I. b
0.28 0.56
0.03 0.00
1.32 1.75
1.03–1.70 1.39–2.19
1
0.08
0.00
1.08
1.07–1.09
1 1 1 1
0.32 0.30 0.40 − 1.12
0.05 0.02 0.03
1.37 1.35 1.49 0.33
1.00–1.88 1.05–1.75 1.04–2.12
− 2 Log likelihood = 1722.51, Nagelkerke R square = 0.15. a The non-significant predictors (coffee, PRS_MSF, and RRS_MSF) in the model are not shown here. b O.R.: Odds ratios; 95% C.I.: 95% confidence interval. c MPI: Maudsley Personality Inventory; RCS_MSF: received crisis support; CHQ: 12-item Chinese Health Questionnaire.
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social impairment and mental morbidity in excessive Internet users (Shapira et al., 2000; Pratarelli and Browne, 2002; Nalwa and Anand, 2003). The personality trait of neuroticism (as defined by the MPI neuroticism score) was associated with Internet addiction (as defined by the CIAS-R) in the freshmen in this study. These students may prefer to do something alone to avoid feeling anxious. Although several previous studies have proposed that the subjects with Internet addiction had distinctive personality characteristics such as depressed, lonely, low in self-esteem, anxious, and bold as well as assertive (Young, 1998a; Beard and Wolf, 2001), Engelberg and Sjöberg (2004) concluded that frequent use of the Internet could not be linked to any specific personality dimension. However, Lee et al. (2005) reported that neuroticism scores may be associated with D2/D3 receptor availability in healthy individuals. It is well known that the dopaminergic system may be implicated in the most important systems involved in addiction behaviors (Nisell et al., 1995). In light of these findings, the association between neuroticism scores and Internet addiction may seem plausible. Male gender was another risk factor for Internet addiction in this study. This observation validates the findings of Young (1998b) and Greenfield (1999). A study on gender differences in sexual arousal found that men tend to be more visual with respect to sexual fantasies while women are more process or verbally oriented (Buss, 1999). As the cost of bandwidths decreased drastically in recent years, the Internet has become more abundant with graphical information. The increased availability of pornography in cyberspace may be one of the reasons for the higher prevalence rate of Internet addiction in males. Estimates suggest that one in five Internet addicts are engaged in some form of online sexual activity (primarily viewing cyberporn and/or engaging in cybersex). Studies also show that men are more likely to view cyberporn. Cooper et al. (2000) studied cybersex compulsives and found that the group was 79% male. The cybersex compulsive group consisted of those who met the criteria for both sexual compulsivity on the Sexual Compulsivity Scale (Kalichman et al., 1994) and who spent more than 11 h a week online engaged in sexual pursuits. However, not all studies about cyber sex addiction agree about the male dominance of the group (Griffiths, 2004). Further studies are thus needed to conclusively validate the link between male gender and Internet addiction. In a survey of Internet addiction in students in India, Nalwa and Anand (2003) found that Internet-dependent users often spend excessive amounts of time online, delaying work and losing sleep due to late-night log-ons.
The disrupted sleep patterns due to late-night Internet sessions may lead to excessive fatigue and thus impair functioning in the academic and occupational realms. In our study, the habit of skipping breakfast is one of the risk factors of Internet addiction. This finding seems reasonable since Internet addicts stay up late at night and may get up too late for breakfast. Nalwa and Anand (2003) also reported that the higher the scores on the loneliness scale, the more dependent on Internet usage the subjects are. Furthermore, Young (1996) demonstrated that addictive use of the Internet led directly to social isolation, depression, familial discord, divorce, academic failure, financial indebtedness, and job loss. These results may suggest a relationship between a poor social support system and Internet addiction. However, further studies are needed to explain why only the RCS score is related to Internet addition. There are several limitations in our study. First of all, we did not conduct individual interviews to confirm the diagnoses of Internet addiction and psychiatric morbidity. Secondly, we only found an association between the risk factors and Internet addiction, but did not clarify the mechanism of these risk factors. Thus the causal relationship cannot be confirmed. Lastly, excluding the freshmen who did not complete all questionnaires from our analyses might have influenced the power of the findings. In conclusion, our findings indicate that Internet addiction is prevalent among university freshmen and certain lifestyle habits and mental health problems are related to Internet addiction. We recommend integrating long-term follow-ups into the associated mental health programs of university freshmen to monitor the development of Internet addiction, as well as prioritizing Internet addiction for further research. Acknowledgments The authors thank Miss Shu Chuan Lin, Miss Ching Lin Chu, Miss Linda J. Chang, Miss Yun-Hsuan Chang and students of the National Cheng Kung University who participated in the study. References Baruch, Y., 2001. The autistic society. Information and Management 38, 129–136. Beard, K.W., Wolf, E.M., 2001. Modification in the proposed diagnostic criteria for Internet addiction. Cyberpsychology and Behavior 4, 377–383. Berkman, L.F., Glass, T., 2000. Social integration, social networks, social support, and health. In: Berkman, L.F., Kawachi, I. (Eds.), Social Epidemiology. Oxford University Press, Oxford, pp. 137–173.
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Psychiatry Research 167 (2009) 294 – 299 www.elsevier.com/locate/psychres
The risk factors of Internet addiction—A survey of university freshmen Hsing Fang Tsai a , Shu Hui Cheng a , Tzung Lieh Yeh a,b , Chi-Chen Shih c , Kao Ching Chen a,b , Yi Ching Yang c,⁎, Yen Kuang Yang a,b a
Department of Psychiatry, National Cheng Kung University Hospital, Tainan, Taiwan Department of Psychiatry, College of Medicine, National Cheng Kung University, Tainan, Taiwan Department of Family Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan b
c
Received 29 January 2007; received in revised form 16 January 2008; accepted 25 January 2008
Abstract This study was designed to explore the risk factors of Internet addiction in 1360 freshmen of the National Cheng Kung University in Taiwan in 2003. The test battery included a self-administrated structured questionnaire, the Chinese Internet Addiction Scale-Revision (CIAS-R), the 12-item Chinese Health Questionnaire (CHQ-12), the Measurement of Support Functions (MSF), and the neuroticism subscale of the Maudsley Personality Inventory (MPI). Of the total study population, there were 680 college freshmen (17.9%) in the Internet addiction group, as defined by high CIAS-R scores. Using logistic regression analyses, we found positive relationships between Internet addiction and male gender, neuroticism scores and the CHQ score. In addition, the freshmen who skipped breakfast and those who had poorer social support also had a higher probability of Internet addiction. Internet addiction is prevalent among university freshmen in Taiwan. Risk factors included male gender, habit of skipping breakfast, mental health morbidity, deficient social support; and neurotic personality characteristics. © 2008 Elsevier Ireland Ltd. All rights reserved. Keywords: Internet; Mental health morbidity; Social support function; Neuroticism
1. Introduction The Internet is fast becoming a basic feature of global civilization. Informative, convenient, and entertaining, the Internet has changed the ways people work and spend their leisure time. As of June 2007, 1.133 billion people used the Internet according to the Internet World Stats. However, uncontrolled Internet use may have negative ⁎ Corresponding author. Department of Family Medicine, College of Medicine, National Cheng Kung University, 138, Sheng Li Road, Tainan 70428, Taiwan. Tel.: +886 6 2766165; fax: +886 6 2091433. E-mail address: [email protected] (Y.C. Yang).
impacts on social, occupational, academic, marital and interpersonal adjustment (Baruch, 2001; Parks, 2002; Engelberg and Sjöberg, 2004). Researchers have used various terms to describe individuals who exhibit addictive behaviors in their Internet use, including “computer dependency,” “online dependency,” “cyber addiction,” “pathological Internet use,” and “Internet addiction disorder” (Whang et al., 2003; Lee and Shin, 2004; Song et al., 2004). Addictive use of the Internet was reported by Young (1996), who found that most people with Internet addiction were young males with low sociality and low self-esteem. Scherer (1997) reported that persons who were over-involved with the Internet
0165-1781/$ - see front matter © 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.psychres.2008.01.015
H.F. Tsai et al. / Psychiatry Research 167 (2009) 294–299
exhibited symptoms of Internet addiction and might have an increased risk of psychiatric morbidity. Chen et al. (2003) also reported that people with addictive behaviors were more likely to have health morbidity, socioeconomic problems and behavioral problems. Certain personality traits should be considered when assessing potential mental problems. In a study involving a large sample, Breslau et al. (1991) reported that neuroticism was one of the significant independent predictors of post-traumatic stress disorder (PTSD). In Taiwanese studies, a positive correlation between a personality trait (namely, neuroticism) and psychiatric morbidity has also been reported (Chen et al., 2001; Yang et al., 2003). Therefore, we speculated that neuroticism is a possible risk factor for Internet addiction. Young people are generally believed to constitute the majority of Internet users. An increasing number of studies have revealed that some youngsters are compulsive in their use of the Internet and exhibit addictive behaviors very similar to those related to alcoholism, substance addiction and pathological gambling (Ng and Wiemer-Hastings, 2005; Ha et al., 2006; Petry, 2006). In Johansson and Götestam's (2004) study, 1.98% of Norwegian youth (aged 12–18) were described as having an “Internet addiction” according to the criteria of Young's (1998b) diagnostic questionnaire. However, few studies have explored the correlation between lifestyle habits, mental health and Internet addiction in young people. Compared with other young people, college students may be more involved with the Internet (Kandell, 1998; Young, 2001). The aim of this study was to investigate risk factors associated with Internet addiction among university freshmen. We hypothesized that risk factors related to personality traits, lifestyle habits, interpersonal relationships and the potential of psychiatric morbidity might be elevated in those who are Internet addicted. 2. Methods 2.1. Subjects A total of 4710 freshmen of the National Cheng Kung University were recruited in 2003. Subjects were enrolled during the routine health examination that was part of freshmen orientation. They were invited to participate in this study in which they would complete a self-administered questionnaire about their personal lifestyle habits and online behaviors. Before the study began, informed consent was obtained from all of the study participants. Since they only agreed to have their questionnaire data and related examination results analyzed anonymously,
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any identifying information was kept confidential. The Ethical Committee for Human Research at the National Cheng Kung University Medical Center approved the study protocol. 2.2. Assessment of personal lifestyle habits Through a self-reported questionnaire, the demographic characteristics, personal history, family history, lifestyle habits, and various medical problems were evaluated. The 14-item questionnaire contains questions about the following systemic diseases: hypertension, diabetes mellitus, tuberculosis, asthma, renal disease, polycystic kidney disease, proteinuria, hematuria, urinary stone, arthritis, epilepsy, systemic lupus erythematosus, polio, and hyperlipidemia. The personal lifestyle habits investigated included breakfast skipping, coffee drinking, alcohol drinking and cigarette smoking. Breakfast skipping was defined in our questionnaires as eating breakfast fewer than three times per week. Similarly, coffee drinking was defined as drinking coffee on more than 4 days per week. In addition, former and current alcohol drinkers were assigned to the alcohol group. Similarly, former and current smokers were assigned to the smoking group. As for the amount of smoking, the number of packs of cigarettes smoked per year was calculated. The number of packs of cigarettes smoked per year was determined by multiplying the number of packs (one pack is equal to 20 cigarettes) smoked per day by 365 days per year and by the duration of smoking in years. 2.3. Measurement of psychological symptoms The instruments in this domain included the Chinese Internet Addiction Scale-Revision (CIAS-R) (Chen et al., 2003), the 12-item Chinese Health Questionnaire (CHQ) (Chong and Wilkinson, 1989), the Measurement of Support Functions (MSF) (Lin et al., 1999; Berkman and Glass, 2000), and the 24-item Neuroticism subscale of the Maudsley Personality Inventory (MPI) (Eysenck and Eysenck, 1975). The CIAS-R is a 4-point, 26-item self-rated measure with good reliability and validity (Chen et al., 2003). This test, which has been used to measure the severity of adolescent Internet addiction, includes 26 questions about the core symptoms and the related problems of Internet addiction, and five background questions about basic demographics, weekly online hours, habitual domains, and experience of Internet utilization (Chen et al., 2003). The core symptoms of Internet addiction included tolerance (four questions), compulsive use (five questions)
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and withdrawal (five questions). The related problems included negative impact on their social activities, interpersonal relationships and physical conditions (seven questions) and time management (five questions). The total scores of the CIAS-R range from 26 to 84, with higher CIAS-R scores indicating increased severity of addiction to Internet activity. The cut-off score of 64 or more in the CIAS-R (Ko et al., 2005) was used to identify those who had Internet addiction. This diagnostic cut-off value of 63/64 has been demonstrated to have a high sensitivity rate (86.6%) and an excellent diagnostic accuracy rate (87.6%). This discriminative potential makes the scale a reliable diagnostic tool in an epidemiological survey, as it can provide the estimated prevalence rate and identify the target case group. The CHQ, which is a 12-item questionnaire, can be considered a culturally sensitive tool for detecting potential psychiatric morbidity among Chinese individuals. The CHQ-12 is a standardized self-reported screening instrument. The CHQ-12 has been used in surveys of minor psychiatric morbidity in three communities in Taiwan (Cheng, 1988). It can be used to identify a “probable clinical case” on the basis of a cut-off score. In addition, it can be used to determine the severity of morbidity on the basis of the total score, which ranges from zero to 12. Higher scores indicate more severe psychiatric morbidity. The sensitivity and specificity in predicting cases of psychiatric morbidity were 69.6% and 98.4%, respectively, in a community study (cut-off = 2/3)
(Cheng, 1988), and 78% and 77%, respectively, in general health clinics (cut-off = 3/4) (Chong and Wilkinson, 1989). In our study, a probable case of psychiatric morbidity was defined using a cut-off value of 3/4. A probable case of psychiatric morbidity was defined as having a score of 4 or higher on the CHQ-12 (Yang et al., 2003). The MSF has four subscales, including perceived crisis support (PCS), perceived routine support (PRS), received crisis support (RCS), and received routine support (RRS) (Lin et al., 1999). Perceived versus received support focuses on the subjective versus the objective continuum of support (Turner and Marino, 1994). Perceived support refers to the perception of the availability of support when it is needed, the appraisal of its adequacy, and the quality of such support. Received support refers to the nature and frequency of specific support transactions. The other major dimensions are routine versus crisis supports. Routine support is the process by which support is received or perceived relative to day-to-day activities, whereas crisis support reflects the process by which support is perceived or received when the ego is confronted with a crisis situation or event. In order to further assess the relationship between Internet addiction and support functions, each of these four subscales was further divided into three categories, including low (≤24 points), moderate (25–34 points), and high (≥35 points) scores. The MPI allows simple investigation of neurotic tendencies and deceptive behaviors. In accordance with
Table 1 Differences between the Internet addiction and non-addiction groups. Predictors
Internet non-addiction
Internet addiction
Chi-square test for predictor
N = 680 (%)
N = 680 (%)
χ2
Demographic and health variables Sex (Male) History of systemic disease a (yes) Coffee drinking (yes) Alcohol drinking (yes) Smoking (yes) Breakfast eating (yes)
463 (68.1) 116 (17.1) 89 (13.1) 10 (1.5) 24 (3.5) 382 (56.2)
483 (71.0) 132 (19.4) 65 (9.6) 13 (1.9) 23 (3.4) 281 (41.3)
Assessments b MPI_Neuroticism (≥25) PCS_MSF (low, moderate) RCS_MSF (low, moderate) PRS_MSF (low, moderate) RRS_MSF (low, moderate) CHQ (≥4)
128 (18.7) 28, 546 (4.1, 80.3) 125, 285 (18.4, 41.9) 38, 411 (5.6, 60.4) 38, 411 (5.6, 60.4) 61 (9.0)
315 (46.3) 46, 533 (6.8, 78.4) 149, 323 (21.9, 47.5) 53, 466 (7.8, 68.5) 53, 446 (7.8, 65.6) 140 (20.6)
a
df
P-value
1.39 1.26 4.22 0.40 0.02 30.02
1 1 1 1 1 1
0.23 0.26 0.04 0.52 0.88 b0.0001
118.47 4.66 12.52 9.97 9.97 36.44
1 2 2 2 2 1
b0.0001 0.09 0.002 0.007 0.005 b0.0001
The systemic diseases included hypertension, diabetes mellitus, tuberculosis, asthma, renal disease, polycystic kidney disease, proteinuria, hematuria, urinary stone, arthritis, epilepsy, systemic lupus erythematosus, polio and hyperlipidemia. b MPI: Maudsley Personality Inventory; PCS_MSF: perceived crisis support; RCS_MSF: received crisis support; PRS_MSF: perceived routine support; RRS_MSF: received routine support; CHQ: 12-item Chinese Health Questionnaire.
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MSF, lower support functions of received crisis, perceived routine and received routine were more prevalent in the Internet addiction group. Because sex was a predictor of Internet addiction in some previous studies (Young, 1996), we selected it and other significant variables to enter in the multiple logistic regression model. As shown in Table 2, male gender increased the risk of Internet addiction by 1.3-fold, and breakfast skipping increased that risk by 1.8-fold. Among the various assessments of psychological symptoms, scores on the neuroticism subscale of the MPI, support functions of received crisis, and the CHQ-12 were significant predictors of the risk of Internet addiction.
the criteria of the MPI, subjects scoring 25 points or higher were categorized as abnormal. 2.4. Statistical methods Group differences in categorical variables were analyzed using the Chi-square test. Subsequently, logistic regression was used to analyze the association between the multiple risk factors and Internet addiction. Variables with P b 0.05 in univariate analyses were included in the multivariate models. The two-tailed significance level was set at 0.05. All of the analyses were carried out using the SPSS software (Version 11, SPSS Inc., Chicago, IL, U.S.A.).
4. Discussion 3. Results The prevalence rates of Internet addiction seem to vary with different study populations. Pallanti et al. (2006) reported that 5.4% of Italian high-school students could be characterized as suffering from Internet addiction as defined by the Internet Addiction Scale criteria. Using the Pathological Internet Use Scale, Morahan-Martin and Schumacher (2000) found that 8.1% of U.S. College students had symptoms related to pathological Internet use. In this study, 17.9% of hr Taiwanese university students were found to be Internet addicted. Owing to the differences in target populations, social and cultural contexts, and screening tools, it is difficult to compare these findings directly. Internet addiction was found to be associated with male gender, habit of skipping breakfast, minor mental health morbidity, poor social support function, and neurotic personality characteristics. Some of these findings are consistent with those of other studies on
Questionnaires were completed by 3806 of the participants completed the questionnaires (response rate = 80.81%), and 67.7% of them were male (males = 67.7%). There were 680 (17.9%) freshmen who were classified as possible cases of Internet addiction. Furthermore, in order to analyze the possible risk factors of Internet addiction, these 680 Internet addiction students and 680 randomly selected nonInternet addiction students from the non-Internet addiction group were included. The distinguishing characteristics of the Internet addiction and non-addiction groups are shown in Table 1. Coffee-drinking and breakfast-eating habits were significantly less common among the students with Internet addiction. Of the psychological symptoms, neuroticism and psychiatric morbidity were significantly associated with Internet addiction. Relative to a high score on the
Table 2 Risk factors of Internet addiction based on the logistic regression analyses (N = 1360). Predictors a
df
Demographic and health variables Sex (male vs. female) Breakfast eating (no vs. yes)
1 1
Assessments c MPI_Neuroticism (≧25 vs.b25) RCS_MSF score Low vs. high Moderate vs. high CHQ (≧ 4 vs. b4) Constant
β
P-value
O.R. b
95% C.I. b
0.28 0.56
0.03 0.00
1.32 1.75
1.03–1.70 1.39–2.19
1
0.08
0.00
1.08
1.07–1.09
1 1 1 1
0.32 0.30 0.40 − 1.12
0.05 0.02 0.03
1.37 1.35 1.49 0.33
1.00–1.88 1.05–1.75 1.04–2.12
− 2 Log likelihood = 1722.51, Nagelkerke R square = 0.15. a The non-significant predictors (coffee, PRS_MSF, and RRS_MSF) in the model are not shown here. b O.R.: Odds ratios; 95% C.I.: 95% confidence interval. c MPI: Maudsley Personality Inventory; RCS_MSF: received crisis support; CHQ: 12-item Chinese Health Questionnaire.
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social impairment and mental morbidity in excessive Internet users (Shapira et al., 2000; Pratarelli and Browne, 2002; Nalwa and Anand, 2003). The personality trait of neuroticism (as defined by the MPI neuroticism score) was associated with Internet addiction (as defined by the CIAS-R) in the freshmen in this study. These students may prefer to do something alone to avoid feeling anxious. Although several previous studies have proposed that the subjects with Internet addiction had distinctive personality characteristics such as depressed, lonely, low in self-esteem, anxious, and bold as well as assertive (Young, 1998a; Beard and Wolf, 2001), Engelberg and Sjöberg (2004) concluded that frequent use of the Internet could not be linked to any specific personality dimension. However, Lee et al. (2005) reported that neuroticism scores may be associated with D2/D3 receptor availability in healthy individuals. It is well known that the dopaminergic system may be implicated in the most important systems involved in addiction behaviors (Nisell et al., 1995). In light of these findings, the association between neuroticism scores and Internet addiction may seem plausible. Male gender was another risk factor for Internet addiction in this study. This observation validates the findings of Young (1998b) and Greenfield (1999). A study on gender differences in sexual arousal found that men tend to be more visual with respect to sexual fantasies while women are more process or verbally oriented (Buss, 1999). As the cost of bandwidths decreased drastically in recent years, the Internet has become more abundant with graphical information. The increased availability of pornography in cyberspace may be one of the reasons for the higher prevalence rate of Internet addiction in males. Estimates suggest that one in five Internet addicts are engaged in some form of online sexual activity (primarily viewing cyberporn and/or engaging in cybersex). Studies also show that men are more likely to view cyberporn. Cooper et al. (2000) studied cybersex compulsives and found that the group was 79% male. The cybersex compulsive group consisted of those who met the criteria for both sexual compulsivity on the Sexual Compulsivity Scale (Kalichman et al., 1994) and who spent more than 11 h a week online engaged in sexual pursuits. However, not all studies about cyber sex addiction agree about the male dominance of the group (Griffiths, 2004). Further studies are thus needed to conclusively validate the link between male gender and Internet addiction. In a survey of Internet addiction in students in India, Nalwa and Anand (2003) found that Internet-dependent users often spend excessive amounts of time online, delaying work and losing sleep due to late-night log-ons.
The disrupted sleep patterns due to late-night Internet sessions may lead to excessive fatigue and thus impair functioning in the academic and occupational realms. In our study, the habit of skipping breakfast is one of the risk factors of Internet addiction. This finding seems reasonable since Internet addicts stay up late at night and may get up too late for breakfast. Nalwa and Anand (2003) also reported that the higher the scores on the loneliness scale, the more dependent on Internet usage the subjects are. Furthermore, Young (1996) demonstrated that addictive use of the Internet led directly to social isolation, depression, familial discord, divorce, academic failure, financial indebtedness, and job loss. These results may suggest a relationship between a poor social support system and Internet addiction. However, further studies are needed to explain why only the RCS score is related to Internet addition. There are several limitations in our study. First of all, we did not conduct individual interviews to confirm the diagnoses of Internet addiction and psychiatric morbidity. Secondly, we only found an association between the risk factors and Internet addiction, but did not clarify the mechanism of these risk factors. Thus the causal relationship cannot be confirmed. Lastly, excluding the freshmen who did not complete all questionnaires from our analyses might have influenced the power of the findings. In conclusion, our findings indicate that Internet addiction is prevalent among university freshmen and certain lifestyle habits and mental health problems are related to Internet addiction. We recommend integrating long-term follow-ups into the associated mental health programs of university freshmen to monitor the development of Internet addiction, as well as prioritizing Internet addiction for further research. Acknowledgments The authors thank Miss Shu Chuan Lin, Miss Ching Lin Chu, Miss Linda J. Chang, Miss Yun-Hsuan Chang and students of the National Cheng Kung University who participated in the study. References Baruch, Y., 2001. The autistic society. Information and Management 38, 129–136. Beard, K.W., Wolf, E.M., 2001. Modification in the proposed diagnostic criteria for Internet addiction. Cyberpsychology and Behavior 4, 377–383. Berkman, L.F., Glass, T., 2000. Social integration, social networks, social support, and health. In: Berkman, L.F., Kawachi, I. (Eds.), Social Epidemiology. Oxford University Press, Oxford, pp. 137–173.
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