1. Background
The internet as a huge network that contains millions of private, public, academic, business, and government channels from local to global scope, with dramatic effects on human life plays a significant role on people’s behavior and mentality (1). Adolescents are the most frequent users of the internet, which among them university students are a group at significant risk of internet addiction (2).
University students are exposed to a new life like inevitable academic use and access to internet, portable mini-computers and cell phones. Additionally, less parental control, feeling of loneliness and isolation which lead to depression and anxiety. On the other hand, some characteristics such as seeking for novelty, competition with peers and the peers pressure, threat them as well as internet addiction (3-7).
Definition of internet addiction is the inability to control one’s internet usage that results in the serious impairment of various aspects of life (8). This term is reported in the appendix of the last version of the diagnostic and statistical manual for mental disorders (DSM-5) as the new phrase, internet gaming disorder (9).
Internet addiction prevalence in college students has been reported to be 16.3% in Italian college students, 4% in the United States, 5.9% and 17.9% in Taiwan, 10.6% in China and 34.7% in Greece (2, 10-13). In university students, there is direct relationship between perceived poor social support and the feeling of social-emotional loneliness with internet addiction (14, 15). Internet addiction is related to mental health state (16). Prevalence of internet addiction in university students in Iran was reported 10 - 43% (2, 17-19).
As personality trait is an important factor for substance dependency, it seems as a significant risk factor for internet addiction (20-23). In this study, our aim is to assess the personality traits in students affected with internet addiction. This can prove the importance of need for a screening tools and help the high risk individual, especially in an academic environment
2. Objectives
Exploration of the prevalence of internet addiction and discerning the role of personality traits as a risk factor of internet addiction, were the main aims of this study. The hypothesis were: 1, demographic characteristics such as sexuality would be positive risk factors for internet addiction; and 2, specific personality traits such as low extraversion, low agreeableness, and low emotional stability would influence the risk of internet addiction. The current research aimed to investigate the scope of effect of three factors including: personality, socio-demographic and Internet usages on internet addiction among medical students.
3. Methods
3.1. Participants
In the current cross sectional research, the statistical sample consisted of all medical students of Shiraz University of Medical Science, Shiraz, Iran. At the time of study, 687 medical students were studied in Shiraz University of Medical Sciences. Among them 364 students was intended to participate in the study. Finally, 278 valid questionnaires were collected. The research was done at second semester of the academic year 1393 - 1394.
The inclusion criteria: All of the medical students studied in 1393 - 1394.
The exclusion criteria: everyone who refused from participating in the study.
3.2. Instruments
Demographic questionnaire consist of questions about age, sex, marital status, student accommodation, year of admission, student residence place.
The internet addiction test (IAT) developed by Kimberly Young is a reliable and valid measure of addictive use of internet. It consists of 20 items ranked on a six options Likert format from never = 0 to always = 5. The minimum and maximum score are zero and 100, respectively. The total score of each participant was categorized into one of these classes: healthy (score 0 - 19), at risk (score 20 - 49), moderate dependence (score 50 - 79) and severe dependence (score 80 - 100) (24). The Persian version of this questionnaire was used in this study (25).
The different causes of internet usage evaluated in a separate questionnaire that contains 10 items.
The 60-item NEO Five-Factor Inventory (NEO-FFI) can define the five basic personality factors. The instrument containing 60 items ranked on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree) that assessed the five-factor model of personality including: neuroticism (N), agreeableness (A), and conscientiousness (C), extraversion (E) and openness (O) factors (26). The Iranian version of this questionnaire was used in this study (26).
3.3. Procedure
All the participants voluntarily participated in this study. The researcher met participants in their classes. After the preliminary introduction about the aims of this study and the confidentiality of disclosure agreement, participants were asked to complete the questionnaires including demographic questionnaire, the ITA questionnaire and immediately afterwards the NEO-FFI.
4. Results
4.1. Descriptive Analysis
Raw data of 278 valid questionnaires were imported into the SPSS version 20 and prepared for the statistical analysis. Mean age of participants was 21.48 ± 2.59.
39% (n = 108) of participants were male and 61% (n = 170) were female. In assessing place of residence, 66% (n = 184) of them lived with family and 34% (n = 94) were living in student residence (Table 1).
Table 1. Demographic Factors Which Affect the Internet Usage
| Prevalence | Internet Usage, % | |
|---|---|---|
| Sex | ||
| Male | 108 | 38.8 |
| Marital status | ||
| Single | 255 | 91.7 |
| Married | 23 | 8.3 |
| Location of residence | ||
| With parent or spouse | 184 | 66.2 |
| Student residence | 94 | 33.8 |
| Year -admission | ||
| 1385 | 33 | 11.9 |
| 1386 | 43 | 15.5 |
| 1387 | 34 | 12.2 |
| 1388 | 32 | 11.5 |
| 1389 | 33 | 11.9 |
| 1390 | 33 | 11.9 |
| 1391 | 35 | 12.6 |
| 1392 | 35 | 12.6 |
| Internet usage | ||
| Scientific research | 178 | 64 |
| Non -scientific research | 159 | 57 |
| chat | 84 | 30.2 |
| Social web | 176 | 63.3 |
| Email checking | 164 | 59 |
| Daily news | 109 | 39.2 |
| Download | ||
| Film | 114 | 41 |
| Music | 148 | 53.2 |
| Software | 114 | 41 |
| Online games | 49 | 17.6 |
4.2. Internet Use
Mean time of Internet usage was 3.81 ± 3.14 hours.
The different causes of internet usage evaluated in a separate questionnaire that contains 10 items. Results are shown in Table 1. The most common usage of internet was scientific search and social network usage; and the least cause was online game and chat.
4.3. Analysis of IAT Score
To analyze the students’ IAT answers, Young’s standard scale was applied. Distribution of severity of internet addiction was as: 45.3% (n = 125) which is in normal range, 51.4% (n = 143) mild internet addiction, 2.9% (n = 8) moderate internet addiction and 0.4% (n = 1) severe addiction.
The assessment of sexuality factor indicated that the males’ scores were higher (M = 27.67, SD = 14.57) than females (M = 20.34, SD = 13.12). Independent t-test analysis indicated IAT scores vary according to gender (P = 0.001).IAT score was significantly higher in students who live with family (M = 24.34) compared to the students who live in student residence (M = 20.92) (P = 0.001). Evaluation of marital status show single students’ IAT mean scores were significantly higher compared to the married students (P = 0.043).
Table 2 show mean and SD of ITA score due to demographic factors in addicted group. There is positive correlation between hours of internet usage and IAT score.
Comparison of IAT mean score between different year of attendance show that students who attended the university at 2012 (1391 Hijri) and 2008 (1387 Hijri) who must participate in university comprehensive tests, respectively show Comprehensive Basic Science Test and comprehensive Pre-Internship test (P = 0.02).
Table 2. Mean of IAT Score and Demographic Factors
| Mean ± SD | P Value | |
|---|---|---|
| Sex | 0.001 | |
| Male | 27.67 ± 14.57 | |
| Female | 20.34 ± 13.12 | |
| Marital status | 0.043 | |
| Single | 24.34 ± 14.89 | |
| Married | 20.92 ± 12.29 | |
| Location of residence | 0.001 | |
| With parent or spouse | 24.1 ± 14.08 | |
| dormitory | 14.4 ± 11.45 | |
| Year of admission | 0.02 | |
| 1385 | 17.66 ± 12.79 | |
| 1386 | 19.13 ± 14.80 | |
| 1387 | 26.52 ± 9.8 | |
| 1388 | 23.78 ± 15.46 | |
| 1389 | 22.18 ± 10.88 | |
| 1390 | 24.96 ± 10.41 | |
| 1391 | 28.57 ± 19.2 | |
| 1392 | 23.48 ± 14.66 |
4.4. Personality Traits and Internet Addiction
The Pearson’s correlation analysis and multiple linear regressions were used to assess the relationship between student personality traits and IAT total scores. The results are shown in Table 3. There is positive correlation between IAT score and neuroticism (N), and negative correlation between IAT score and, agreeableness (A), and conscientiousness (C), extraversion (E). No significant relationship was found between IAT total scores and openness personality traits. The investigation of the potential role of personality traits in explaining problematic Internet usage, was done by multiple regression analysis. The IAT total scores were set as dependent variables. The results of the multiple linear regressions analyses shows that the only domain that could predict internet addiction was agreeableness (A) which could predict 0.1% of internet addiction variable regression is calculated by : y = ax + b, so predictive formula for internet addiction can be : Y = 46.21 ± 0.762 (Agreeableness). Raw score of agreeableness can be put in this formula and internet addiction can be predicted.
Table 3. The Correlation Coefficient Between the Personality Traits and IAT Scores
| Personality traits | |
|---|---|
| Neuroticism (N) | |
| The correlation coefficient | +0.2** |
| The significance level | 0.001 |
| Extraversion (E) | |
| The correlation coefficient | -0.118* |
| The significance level | 0.05 |
| Openness (O) | |
| The correlation coefficient | 0.043 |
| The significance level | 0.478 |
| Agreeableness (A) | |
| The correlation coefficient | -0.379** |
| The significance level | 0.001 |
| Conscientiousness (C) | |
| The correlation coefficient | -0.21** |
| The significance level | 0.001 |
Comparison of personality traits between addicted and non-addicted groups is reported in Table 4. Non-addicted group show significant higher mean score in agreeableness (A), and conscientiousness (C), extraversion (E).neuroticism score was significantly higher in addicted group.
Table 4. Mean of Personality Traits of Internet Addicted and Non-Addicted Populations
| Personality Traits | Fill Out This | Fill Out This | Fill Out This |
|---|---|---|---|
| Neuroticism (N) | 3.94 | 0.001 | |
| Non-addict | 19.30 ± 7.87 | ||
| Addict | 22.83 ± 7.03 | ||
| Extraversion (E) | 2.53 | 0.01 | |
| Non-addict | 29.0 ± 6.84 | ||
| Addict | 26.95 ± 6.61 | ||
| Openness (O) | 0.386 | 0.7 | |
| Non-addict | 25.38 ± 5.89 | ||
| Addict | 25.63 ± 5.2 | ||
| Agreeableness (A) | 5.54 | 0.001 | |
| Non-addict | 32.27 ± 5.91 | ||
| Addict | 28.51 ± 5.4 | ||
| Conscientiousness (C) | 2.83 | 0.005 | |
| Non-addict | 33.32 ± 7.49 | ||
| Addict | 30.90 ± 6.70 |
5. Discussion
The main purpose of this study was to investigate the risk of internet addiction in medical students by consideration of the interplay between demographic data, student Internet usage and personality traits. The prevalence was higher compared to the other similar research in university students in Iran and other countries. Internet addiction prevalence in college students has been reported to be 4% in the United States, 5.9% and 17.9% in Taiwan, 10.6% in China and 34.7% in Greece. In other Iranian medical university prevalence was between 5.2 to 22% (2, 10-13, 17-19, 27). Although this difference can be related to increasing rate of accessibility of technology. This high rate of internet addiction is worrying. In our study, the most common internet usage among medical student was intended to search for the scientific articles. This was confirmed in the study of medical students (17) although the most common purpose of excessive internet usage in other studies is social cyber connection such as chatting (10, 27).
In this study similar to other researches male students achieved higher means IAT scores than females (17, 26, 28). A few studies indicate that internet addiction rate was higher in female students (10, 29).This can be explained by the men’s interest and motivation for information technology. Culture can also have a significant role in such an outcome.
Our research show that the mean IA score was higher in those who lived with family in comparison with the students who lived at student residence. This finding is similar to the other studies (26). This can be due to more sense of responsibility in students who live at student residence as they have to manage everything in their own life.
Well known risk factor which is defined in this research for IA, is being single. In other similar studies being single, having impaired family relationships and being divorced were risk factors for internet addiction (28). This can be explained by cognitive behavioral model that justify this finding. Being online give individuals the sense of competence and socialization that consequently influence the internet usage (13). Beyrami et al. studied the effect of perceived social support and the feeling of social-emotional loneliness on internet addiction in university students (15). This was also approved in shaw’s study (14).
In this research, initial hypothesis of influence of personality traits as a predictor for internet addiction was partially accepted. In our study, there was positive correlation between IAT score and neuroticism (N), and Negative correlation between IAT score and, agreeableness (A), conscientiousness (C), and extraversion (E). No significant relationship was found between IAT total scores and Openness personality traits. Different studies use various type of personality assessment tools. Among those the one that use five factors model and three factors model confirmed the effect of neuroticism (N) on internet addiction (29-34). Negative correlation of agreeableness (A), conscientiousness (C), extraversion (E) are similar with findings in the other studies assessing personality role in internet addiction (20, 30, 31). Three independent British samples on the NEO-FFI indicate that agreeableness, neuroticism and conscientiousness are more reliable sub-scales than extraversion and openness to experience and extraversion (35).
Neuroticism is the susceptibility to experience negative feelings, such as depression, anxiety, anger with low tolerance for stress or unpleasant stimuli. Those with high score in neuroticism interpret usual situations as alarming and threatening. These problems in emotional regulation can influence the ability of thinking clearly, making decisions, and coping effectively with stress (36). These can be the reason that these individuals use substitute methods like internet usage in dealing with stressful situations. This can be an explanation for increasing rate of internet addiction in periods before comprehensive tests during academic year.
The agreeableness trait was a dramatic negative predictor of internet addiction. Persons with low agreeableness have some problems in establishing real interpersonal relationships, or sharing team-work experiences, thus they prefer to spend their free time to surf the Internet (37, 38) and this is a mean to satisfy their personal needs.
Another personality trait that showed a significant negative effect in predicting internet addiction was extraversion. Extraversion is characterized by attention seeking, being talkative, having high positive affect and sociability in real life whereas introverts are over-aroused and nervous. They are therefore in need of peace and calm environment to be in the optimal level of performance; so they may prefer interacting online with others (39).
Conscientiousness personality trait was also a significant negative predictor of internet addiction. So students with a methodical and structured behavior in comparison to disorganized persons have a lower risk of Internet addiction (40).
Another interesting finding in this research was the effect of stressors such as comprehensive basic science test and comprehensive pre-internship test on increasing internet usage. It seems that students use this maladaptive behavior as a defense mechanism to escape from these stressors. Students in 4th and 10th semester need to be trained correctly and efficiently in order to deal with stress in critical condition and also to maintain positive academic performance.no similar study was found to assess this effect.
These data were a good identifier of medical students of Medical faculty of Shiraz University of Medical Sciences. Several limitations in this study should be underlined. The data are related to students from one specific Iranian medical university; hence, this can limit its generalization. However, the same opportunities in using information and communication technologies in all medical students in Iran can explain the minimal homogeneity among students in internet usage. It is recommended that initial assessment of medical students’ personality by screening tools and identification of populations at risk, may prove the need for favorable methods for initiation of prevention.
