Background
Internet addiction (IA) has increased with the rapid spread of Internet use in recent years. According to a survey by the Ministry of Internal Affairs and Communications in 2016, over 90% of people ages 13 to 60 years old use the Internet in Japan [
1]. IA has been found to be uncontrollable and damaging with excessive use of this technology [
2]. Previous reports have shown that IA is one of the “impulse-control disorders” [
3]. In 2013, the diagnosis of “Internet gaming disorder” was categorized as one of the problematic addictive behaviors in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [
4]. Furthermore, it has been argued that “gaming disorder,” including Internet gaming, should be included as a new concept in the International Classification of Disease, Eleventh edition (ICD-11) [
5]. Thus, IA will be globally considered a disorder in the near future. It is important to prevent it from becoming a problem of the new era.
Recently, the Ministry of Education, Culture, Sports, Science and Technology—Japan (MEXT) has reported that many teachers and staff members working at schools had stress and poor mental health; therefore, the number of their school workers with mental illness had increased. One of the related symptoms of poor mental health is burnout syndrome (BOS). School teachers with BOS tend to have many causes; for example, teachers are asked to take on extra work, work at a higher quality, and spend an excessive amount of time on work. They work independently in spite of problems and find it hard to receive supportive feedback for their efforts [
6]. As a result, it is well known that teachers experience burnout more easily. Maslach et al. developed the Maslach Burnout Inventory (MBI) [
7] and defined BOS with three sub-concepts: “emotional exhaustion,” “depersonalization,” and “personal accomplishment” that can occur among individuals who work with people in some capacity. MBI was used for many studies all over the world after being issued [
8,
9]. It has been contributed to the burnout research, allowing to be modified by researchers. In Japan, Kubo and Tao made the Japanese Burnout Scale (JBS) based on the MBI [
10].
In a previous study about IA and BOS, it found a relationship between IA and BOS with Avci and Sahin reporting that BOS has a positive correlation with IA using an Internet addiction scale in Turkey among university healthcare staff members [
11]. Another paper of adolescents reported that BOS indicates excessive Internet use, as well as BOS being caused by excessive Internet use [
12]. Inaba et al. published a study of medical students in which the BOS group had a higher risk of IA by K-scale than the non-BOS group [
13,
14].
MEXT has promoted incorporating Information and Communications Technology (ICT) in schools and improving the ICT environment at school. This policy has a purpose not only to educate teachers about how to create more attractive lessons and give them more knowledge for ICT utilization among students, but also to reduce the work burden among teachers with effective ICT [
15]. Teachers will have increased opportunities to use the Internet in the future. Previous studies have indicated long time use of the Internet caused IA. [
16]. A previous epidemiological study showed that at-risk Internet users were approximately 5% among high school teachers in a rural prefecture of Japan [
17]. We are afraid that IA will increase among teachers in the near future. However, there have been few studies about the relationship of at-risk IA and BOS among teachers on a nationwide scale.
This study aims to research the relationship between at-risk IA and the Internet usage or BOS by conducting a nationwide cross-sectional survey and examining the factors associated with IA. Our findings will contribute to improving school mental health, including Internet addiction, among teachers.
Results
In our study, the at-risk IA group had 96 respondents (5.7%) and the non-IA group had 1600 respondents (94.3%). Table
1 shows the characteristics of respondents between the at-risk IA group and non-IA group. The at-risk IA group was younger and had a shorter length of service in school than those in the non-IA group. Gender and position at school had no relationship. In the BOS score, emotional exhaustion and depersonalization had higher scores in the at-risk IA group than in the non-IA group. However, decline of personal accomplishment had a lower score in the at-risk IA group than in the non-IA group.
Table 1
Characteristics of respondents or burnout score with at-risk Internet addiction(IA) and non-IA among junior high school teachers
Age (years)†, n(%) |
< 30 | 312 (19.50) | 42 (43.75) | < 0.001 |
30–39 | 363 (22.69) | 18 (18.75) | |
40–49 | 364 (22.75) | 18 (18.75) | |
50 < | 561 (35.06) | 18 (18.75) | |
Gender‡, n(%) |
Male | 942 (58.88) | 62 (64.58) | 0.269 |
Female | 658 (41.13) | 34 (35.42) | |
Position at school‡, n(%) |
Administrator | 129 (8.06) | 5 (5.21) | 0.386 |
Senior teacher | 60 (3.75) | 3 (3.13) | |
Teacher | 1160 (72.50) | 67 (69.79) | |
Full-time lecturer | 131 (8.19) | 11 (11.46) | |
Nursing teacher | 60 (3.75) | 3 (3.13) | |
Non-permanent teacher | 60 (3.75) | 7 (7.29) | |
Duration of service (year)†, n (%) |
< 5 | 276 (17.25) | 37 (38.54) | < 0.001 |
5–9 | 258 (16.13) | 16 (16.67) | |
10–19 | 328 (20.50) | 14 (14.58) | |
20–29 | 378 (23.63) | 17 (17.71) | |
30 ≦ | 360 (22.50) | 12 (12.50) | |
Burnout§, mean ± SD |
Emotional exhaustion | 2.54 ± 0.88 | 3.04 ± 0.93 | < 0.001 |
Depersonalization | 1.67 ± 0.62 | 2.10 ± 0.75 | < 0.001 |
Decline of personal accomplishment | 3.35 ± 0.71 | 3.15 ± 0.66 | 0.009 |
Table
2 shows comparisons of Internet usage between the at-risk IA group and the non-IA group. Mean IAT scores were 46.68 ± 6.23 in the at-risk IA group and 25.51 ± 4.82 in the non-IA group. The at-risk IA group had a higher prevalence of having a smartphone. The at-risk IA group used the Internet more for gaming, hobbies or entertainment, shopping, and surfing the Internet than the non-IA group. The at-risk IA group spent a longer time on the Internet regardless of day of the week or purpose than the non-IA group.
Table 2
Comparisons of internet usage between at-risk IA and non-IA
IAT score, Mean ± SD | 25.51 ± 4.82 | 46.68 ± 6.23 | |
Device for Internet access ‡, n(%)※ |
Smartphone | 1226 (76.63) | 84 (87.50) | 0.014 |
Laptop computer | 1337 (83.56) | 78 (81.25) | 0.554 |
Desktop computer | 648 (40.50) | 45 (46.88) | 0.217 |
Tablet | 524 (32.75 | 36 (37.50) | 0.336 |
Feature phone | 193 (12.06) | 9 (9.38) | 0.430 |
Others | 17 (1.06) | 4 (4.17) | |
Number of devices§, mean ± SD | 2.47 ± 0.86 | 2.67 ± 0.87 | 0.026 |
Activity on Internet‡, n(%)※ |
Hobbies or entertainment | 1078 (67.38) | 85 (88.54) | < 0.001 |
Information gathering(work) | 1389 (86.81) | 82 (85.42) | 0.695 |
Information gathering(private) | 1280 (80.00) | 78 (81.30) | 0.766 |
Communication(private) | 1141 (71.31) | 77 (80.21) | 0.060 |
Communication(work) | 1093 (68.31) | 71 (73.96) | 0.247 |
Shopping | 802 (50.13) | 66 (68.75) | < 0.001 |
Surfing the Internet | 416 (26.00) | 56 (58.33) | < 0.001 |
Gaming | 308 (19.25) | 44 (45.83) | < 0.001 |
Investment or gambling | 41 (2.56) | 4 (4.17) | 0.342 |
Others | 18 (1.13) | 0 (0.00) | |
Time spent on Internet access† |
Weekdays use for work |
Not used | 17 (1.06) | 0 (0.00) | < 0.001 |
< 60 min | 1189 (74.31) | 57 (59.38) | |
60 min ≦ | 394 (24.63) | 39 (40.63) | |
Weekdays use for private |
Not used | 33 (2.06) | 0 (0.00) | < 0.001 |
< 60 min | 1110 (69.38) | 21 (21.88) | |
60 min ≦ | 457 (28.56) | 75 (78.13) | |
Weekends use for work |
Not used | 230 (14.38) | 12 (12.50) | 0.001 |
< 60 min | 1125 (70.31) | 52 (54.17) | |
60 min ≦ | 245 (15.31) | 32 (33.33) | |
Weekends use for private |
Not used | 36 (2.25) | 0 (0.00) | < 0.001 |
< 60 min | 922 (57.63) | 12 (12.50) | |
60 min ≦ | 642 (40.13) | 84 (87.50) | |
Table
3 shows the means of IAT scores according to the quartiles of 3 factors of JBS. The emotional exhaustion and depersonalization were positively associated with IAT scores. However, the highest quartile of decline of personal accomplishment had the lowest IAT scores. There was a statistically significant relationship between the IAT scores and each factor of BOS.
Table 3
Internet Addiction Test scores by quartile of each factor of burnout
Emotional exhaustion |
Quartile 1 | 1.00–1.80 | 425 | 24.7 | 5.70 | < 0.001 | < 0.001 |
Quartile 2 | 1.81–2.40 | 458 | 26.2 | 5.97 | | |
Quartile 3 | 2.41–3.20 | 455 | 27.4 | 6.84 | | |
Quartile 4 | 3.21–5.00 | 358 | 28.8 | 8.58 | | |
Depersonalization |
Quartile 1 | 1.00–1.17 | 469 | 24.5 | 4.87 | < 0.001 | < 0.001 |
Quartile 2 | 1.18–1.50 | 442 | 26.2 | 6.39 | | |
Quartile 3 | 1.51–2.00 | 421 | 27.4 | 6.88 | | |
Quartile 4 | 2.01–5.00 | 364 | 29.4 | 8.66 | | |
Decline of personal accomplishment |
Quartile 1 | 1.00–2.83 | 347 | 27.1 | 7.35 | < 0.001 | 0.020 |
Quartile 2 | 2.84–3.33 | 401 | 27.3 | 7.48 | | |
Quartile 3 | 3.34–3.83 | 438 | 27.1 | 7.01 | | |
Quartile 4 | 3.84–5.00 | 510 | 25.7 | 5.94 | | |
Table
4 shows the odds ratio and 95% confidence interval of each risk factor with at-risk IA determined using multiple logistic regression analysis. Internet activity such as gaming and surfing the Internet, time spent on Internet access on both weekdays and weekends for private purposes for 1 h or more, and depersonalization were positively associated with at-risk IA. On the other hand, highest quartile for decline of personal accomplishment had a significantly lower odds ratio with at-risk IA.
Table 4
Multivariate odds ratio and 95% confidence intervals for at-risk IA group
Age |
50 < | 1.00 | |
40–49 | 1.19 | 0.57–2.47 |
30–39 | 0.73 | 0.34–1.57 |
< 30 | 1.42 | 0.71–2.84 |
Device for Internet access |
Smartphone (no) | 1.00 | |
Smartphone | 0.84 | 0.41–1.75 |
Activity on Internet |
Hobbies or entertainment (no) | 1.00 | |
Hobbies or entertainment (yes) | 1.34 | 0.65–2.77 |
Shopping (no) | 1.00 | |
Shopping (yes) | 1.18 | 0.71–1.97 |
Surfing the Internet (no) | 1.00 | |
Surfing the Internet (yes) | 1.88 | 1.16–3.05 |
Gaming (no) | 1.00 | |
Gaming (yes) | 1.83 | 1.13–2.98 |
Time spent on Internet access |
Weekdays use for work 60 min > | 1.00 | |
Weekdays use for work 60 min ≦ | 0.83 | 0.48–1.42 |
Weekdays use for private 60 min > | 1.00 | |
Weekdays use for private 60 min ≦ | 3.30 | 1.69 — 6.44 |
Weekends use for work not used | 1.00 | |
Weekends use for work 60 min > | 0.68 | 0.33–1.40 |
Weekends use for work 60 min ≦ | 0.99 | 0.43–2.25 |
Weekends use for private 60 min > | 1.00 | |
Weekends use for private 60 min ≦ | 2.71 | 1.19–6.18 |
Burnout |
Emotional exhaustion quartile 1 | 1.00 | |
Emotional exhaustion quartile 2 | 1.12 | 0.49–2.57 |
Emotional exhaustion quartile 3 | 1.07 | 0.47–2.44 |
Emotional exhaustion quartile 4 | 2.07 | 0.87–4.91 |
Depersonalization quartile 1 | 1.00 | |
Depersonalization quartile 2 | 3.21 | 1.31–7.87 |
Depersonalization quartile 3 | 3.29 | 1.29–8.36 |
Depersonalization quartile 4 | 6.04 | 2.36–15.47 |
Decline of personal accomplishment quartile 1 | 1.00 | |
Decline of personal accomplishment quartile 2 | 0.90 | 0.47–1.70 |
Decline of personal accomplishment quartile 3 | 0.85 | 0.45–1.60 |
Decline of personal accomplishment quartile 4 | 0.38 | 0.19–0.78 |
Discussion
Our study proved that at-risk IA was associated with the use of the Internet for many hours both on weekdays and weekends in private, using gaming and surfing the Internet. Furthermore, our findings indicated significant relationships between at-risk IA and two factors of BOS: depersonalization and decline of personal accomplishment even adjusting for those variables. These findings were clarified as the current situation of school mental health among teachers by a nationwide epidemiological survey.
The relationship between long-time Internet use and IA has been clarified in previous studies [
12,
19]. Especially, Internet use for private purpose is more risky, and playing games and surfing the Internet can lead to overuse of the Internet. Many studies have revealed that the gaming is significantly related to the IA [
29], but, to our knowledge, the study that surfing the Internet is related to the IA could not be found. It is well known that long-term use of the Internet is an IA risk factor. Tsumura et.al reported in the study researched in rural areas in Japan that the at-risk IA group spent a long time on the Internet [
17]. We investigated on a nationwide scale, not in one area; then, the same result was obtained. In the present study, the Internet usage for a long time privately both weekdays and weekends were of higher risk than using the Internet for less than 60 min. When surfing the Internet, people who enjoy surfing the Internet, using the Internet without purpose, may easily lead to long time use.
Nanda et al. reported that BOS caused some addictive behaviors [
30]. There were some previous studies of the relationship between BOS and addiction among employees [
7,
11,
31‐
33]. Of these studies, they suggested that there were those associations between BOS and alcohol dependence or substance abuse. We found that those with BOS may have behavioral dependence such as Internet usage. BOS may easily lead to addictive behavior.
In factors of BOS in the study by Avci and Sahin [
11], depersonalization was positively related to IA, and “diminished self-accomplishment” was negatively related to IA. Diminished self-accomplishment has the same meaning as decline of personal accomplishment in our study, and these results were similar to our findings. These previous studies [
12‐
14] clarified the relationship between BOS and IA among youths. We found these relationships among teachers by a nationwide random sample. We clarified the positive relationship with depersonalization of BOS and IA, even adjusting for characteristics and Internet usage. Our findings proved the relationship of depersonalization and IA by multivariate analysis.
Okayasu reviewed previous research of IA as a psychosocial consequence and risk factors involved [
26]. He described the risk factors of IA stemming from feelings of loneliness, lack of social skills, and lack of social support. He indicated IA was one coping mechanism for psychosocial stress. Depersonalization has been defined as “an unfeeling and impersonal response toward recipients” [
7]. It has been reported that people with depersonalization have weaker communication or social skills. Depersonalization is one of interpersonal relationship disorders, and it is easy to show escape from the reality or aggression [
10] With regard to interpersonal relations, Internet gaming disorder (IGD) was associated with problems with peers, a higher prevalence of both being bullied and bullying others [
28]. Our study also indicated the positive relationship between higher scores of depersonalization and at-risk IA. Our result showed a reasonable result similar to previous studies. If IA occurs when coping with psychosocial stress, teachers work hard and they become exhausted both mentally and physically. They are imprisoned in the world of the Internet in trying to escape from real problems, and as a result, they may feel troubled or not be able to build better relationships with their students and colleagues. On the other hand, the personality characteristics of at-risk IA respondents included impulsively approaching new stimuli while being emotionally unstable with higher anxiety levels, and tending to avoid communication with others in real society. Yen et al. suggest three mechanisms to account for the association between IA and psychiatric symptoms [
34]. First, psychiatric symptoms may lead to the onset or persistence of IA. Second, IA may precipitate psychiatric symptoms. Third, IA and psychiatric symptoms may increase vulnerability to each other. Anxiety, depression, and attention-deficit hyperactivity disorder (ADHD) are known as psychiatric symptoms associated with IA [
35‐
37]. We can easily guess that people with these symptoms tend to have depersonalization, and then they are not able to build good interpersonal relationships. Our results suggest that findings of early-stage depersonalization may prevent or screen IA among junior high school teachers. It is important to pay attention to teacher behavior and note changes toward depression or aloofness to students, parents, and colleagues, or a refusal to connect with co-workers and relations, instead devoting their time to using the Internet.
Sano et al. reported an association between decline of personal accomplishment and social activity disorder [
38]. It indicates that decline of personal accomplishment is against IA. It seems that teachers with decline of personal accomplishment tend to reject contact with real society in addition to society via the Internet. Furthermore, it is said that decline of personal accomplishment is less related to a stress factor [
21]. Kubo suggested that decline of personal accomplishment such as factors other than the stressor derived from the workplace environments, perhaps experiences and personal characteristics are involved in the decline of personal accomplishment feeling [
19]. Teachers who cannot obtain support from other teachers have been reported to be prone to decline of personal accomplishment in Japan [
19]. Previous studies have reported that there was a relationship between IGD and individual aspects of psycho-social tendencies, such as loneliness, low self-esteem, low self-efficacy, low life-satisfaction, and lower educational and career attainment [
29]. Although we cannot clarify the causal relationship in this study, not only the workplace environment but also the individual characteristics of teachers may cause burnouts and depend on the Internet world. Our study found a highest quartile for decline of personal accomplishment had lower odds ratio with at-risk IA. Thus, we guess that teachers with at-risk IA may have higher personal accomplishments in the Internet world.
This study has several limitations. First, our study used a cross-sectional design, which does not prove a causal relationship between BOS and IA. Second, since there is no consensus on the formal diagnostic criteria and gold standard measures for IA, the diagnostic accuracy properties of the IAT cut-off scores are yet to be established. Third, we used the JBS that was developed based on the MBI using situations of working people in human service occupations, and it was used in many studies in Japan. However, it was difficult to compare this study in detail with worldwide studies. Fourth, this study was a national survey, but we obtained a low response rate. We need to conduct a study with a higher response rate. Furthermore, we did not pose questions which vary depending on the work of the junior high school teachers, and these items may influence BOS. Then, we could not distinguish between personal accomplishment in the real world and in the Internet world. Since there may be differences between males and females in how to use the Internet, it should be examined in detail by gender. Finally, our results may be underestimated as we cannot rule out the possibility that there was some rejection to reply to the questionnaire as a result of heavy IA or BOS.