Vol.3, No.6, 407-412 (2013) Open Journal of Preventiv e Me dic ine
Relationships of loneliness and mobile phone
dependence with Internet addiction in Japanese
medical students
Satoko Ezoe1*, Masahiro Toda2
1Shimane University, Health Service Center Izumo, Shimane, Japan; *Corresponding Author: satoezoe@med.shimane-u.ac.jp
2Department of Pharmacology, Osaka Dental University, Osaka, Japan
Received 25 July 2013; revised 12 August 2013; accepted 19 August 2013
Copyright © 2013 Satoko Ezoe, Masahiro Toda. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
We investigated factors contributing to Internet
addiction in 105 Jap a nese medical students. The
subjects were administered by a self-reporting
questionnaire designed to evaluate demogra-
phic factors, Internet addiction, loneliness, health-
related lifestyle factors, depressive state, pat-
terns of behavior, and mobile phone depend-
ence. Results of multivariate logistic regression
analysis indicated that loneliness and mobile
phone dependence were positively related to
degree of addiction. Our findings suggest that
Internet addiction is associated with loneliness
and mobile phone dependence in Japanese
Keywords: Internet Addiction; Mobile Phone
Dependence; Loneliness ; Depression; Medical
The Internet rapidly developed and came into wide-
spread use in Japan in the mid-1990s, and has since be-
come an established part of daily life. In 2011, there were
96 million users, which correspond to a penetration rate
of about 80% [1]. While this new information and com-
munication technology resource is convenient and popu-
lar, various social issues have arisen, including changes
in interpersonal relationships, leaks of private informa-
tion, Internet swindles, excessive use, and even depend-
In the Diagnostic and Statistical Manual of Mental
Disorders (DSM) [2], two concepts are used to define
aspects of dependence in regard to substance abuse; be-
havioral and physical. In behavioral dependence, sub-
stance-seeking activities and related evidence of patho-
logical use patterns are emphasized, whereas physical
dependence refers to the physical effects of multiple
episodes of substance use. In definitions stressing physi-
cal dependence, ideas of tolerance or withdrawal appear
in the classification criteria [2,3], and the term addiction
is nearly the same concept as dependence. Griffiths re-
ported elements of Internet addiction, such as tolerance,
withdrawal symptoms, and recurrence [4], while psy-
chological dependence (addiction) on information tech-
nology (IT), such as the Internet and mobile phones—
also referred to as habituation—is characterized by ex-
cessive use and an intermittent craving for IT-related
According to Young, Internet addiction is defined as
excessive time spent on Internet-related activities, an
increasing tolerance to the effects of being online, un-
pleasant feelings when off-line, and denial of associated
problematic behaviors [5]. Previous studies showed that
Internet addiction is associated with loneliness [6-20],
depression [10,11,19-25], poor self-esteem [7,10,26],
shyness [27], and low life satisfaction [15,16]. However,
few multivariate studies have been conducted to investi-
gate the correlation of Internet addiction with loneliness,
health-related lifestyle factors, patterns of behavior, de-
pressive state, and mobile phone dependence. In particu-
lar, because mobile phone use for IT purposes has be-
come widespread in Japan, it is considered necessary to
examine the relationship between Internet addiction and
mobile phone dependence.
In previous studies, we found that mobile phone de-
pendence is associated with unhealthy lifestyle factors
[28-30], extrovert or neurotic personality traits [29], ma-
ternal affectionate constraint in childhood [31], Type A
behavior traits [30], and depression [30], which might be
Copyright © 2013 SciRes. OPEN A CCESS
S. Ezoe, M. Toda / Open Journal of Preventive Medicine 3 (2013) 407-412
related to Internet addiction. On the other hand, few re-
ports have been presented regarding the relationship be-
tween Internet addiction with personal computer use and
mobile phone dependence. In the present study, we util-
ized multivariate logistic regression analysis to examine
the association of demographic characteristics, loneliness,
health-related lifestyle factors, depressive state, patterns
of behavior, and mobile phone dependence with Internet
2.1. Subjects
We recruited 139 freshmen university students from
the Faculty of Medicine at Shimane University in Japan.
After the protocol received the approval of the institute’s
review board and informed consent was obtained from
each participant, the subjects filled out a set of self-re-
porting questionnaires designed to evaluate Internet ad-
diction, health-related lifestyle factors, loneliness, de-
pressive state, behavior patterns, and mobile phone de-
pendence. Answers from 105 of the respondents (40
males, 65 females) who accessed the Internet with a per-
sonal computer and properly completed all the question-
naire items were statistically analyzed. The mean (±SD)
ages for the males were 19.3 ± 2.0 and for the females
were 18.7 ± 1.0 years.
2.2. Internet Addiction
Internet addiction was evaluated using Young’s Inter-
net Addiction Test (IAT) [5], a self-rating questionnaire
that consists of 20 items. Each response was scored on a
Likert scale (1, 2, 3, 4, 5), with the scores then summed
to provide a quantitative overall Internet addiction score
ranging from 20 to 100. Higher scores indicated a greater
level of addiction. According to Young’s criteria, re-
spondents with scores greater than 40 were categorized
as addict (including probable addict).
2.3. Loneliness
Loneliness was evaluated using the UCLA Loneliness
Scale (Version 3) [32]. This is a 20-item self-report in-
ventory measured using a Likert scale (1, 2, 3, 4) with a
total score ranging from 20 to 80 and high scores indi-
cating a high level of loneliness.
2.4. Depressive State
Depressive state was assessed using the Beck Depres-
sion Inventory-II (BDI-II) [33], a self-rating question-
naire that consists of 21 items with a total score ranging
from 0 to 63. Higher scores indicate greater levels of
depression. The reliability and usefulness of the Japanese
version of the BDI-II have been confirmed [34]. Our
subjects were categorized as having either no or minimal,
or mild or greater depression based on this score (cutoff
point 13/14) [33].
2.5. Behavior Patterns
Type A behavior patterns vary according to culture and
nationality [35]. Therefore, we assessed patterns of be-
havior using the Tokai University Type A Pattern Scale
[35,36], which was designed for a Japanese population.
The scale consists of 11 items with a total score ranging
from 0.25 to 98.75. Our subjects were categorized as
having either a Type A or Type B behavior pattern, with
those scoring greater than 43.1 points placed in the Type
A category [36].
2.6. Health-Related Lifestyle
In the Alameda Country Study performed in the Unit-
ed States, 7 health practices were shown to be signifi-
cantly related to physical health status and, subsequently,
mortality rate [37]. Based on that study and after taking
into consideration differences in cultural backgrounds,
we revised the list of 7 practices to 8 items for Japanese
respondents [38,39].
We awarded higher points for responses that indicated
better lifestyle choices in regard to health, with the over-
all rating for each respondent derived from 8 lifestyle
items; smoking habit, drinking habit, daily consumption
of breakfast, appropriate daily duration of sleep and work,
regular physical activity, appropriate levels of stress, and
a nutritionally balanced diet. Each item had multiple
answers (2 - 6 each), and the answers were dichotomized
into a “good” or “not good” health practice [30]. Scores
from the 8 “good” items were totaled to provide an index
of cumulative personal health practices, or Health Prac-
tice Index (HPI). Respondents with 6 - 8 points were
allocated to the good and those with 0 - 5 points to the
poor category [30,40].
2.7. Mobile Phone Dependence
Mobile phone dependence was evaluated using the
Mobile Phone Dependence Questionnaire (MPDQ) [41],
a self-rating questionnaire that consists of 20 items. Each
response was scored on a Likert scale (0, 1, 2, 3). Likert
scores for each item were then summed to provide a
quantitative overall mobile phone dependence score
ranging from 0 to 60. Higher scores were considered to
indicate greater dependence.
2.8. Statistical Analysis
Univariate and subsequent multivariate logistic re-
gression analyses were applied to identify possible asso-
ciations between Internet addiction and each factor
(demographic characteristics, loneliness, depressive state,
Copyright © 2013 SciRes. OPEN A CCESS
S. Ezoe, M. Toda / Open Journal of Preventive Medicine 3 (2013) 407-412 409
health-related lifestyle, patterns of behavior, mobile
phone dependence). All variables with a p value < 0.2 in
univariate analysis were included in the forward stepwise
multivariate analysis (likelihood ratio) after age adjust-
ment. Statistical significance was set at p < 0.05 and the
confidence interval was 95%.
Table 1 shows the results for each questionnaire.
Scores for Internet addiction ranged from 20 to 87 (mean
38.4), while the frequency of addict students was 42.9%.
Ta b l e 2 shows personal characteristics related to the In-
ternet addiction category.
Univariate logistic regression results revealed a statis-
tically significant relationship between Internet addiction
and university department (OR = 2.53, p < 0.05), depres-
sive state (OR = 4.33, p < 0.005), mobile phone depend-
ence (OR = 1.05, p < 0.05), and loneliness (OR = 1.12, p
< 0.001) (Ta bl e 3 ). In addition to those variables, mode
of residence and pattern of behavior (variables with p
value < 0.2) were included in the age-adjusted multivari-
ate logistic regression analysis. Using a forward stepwise
procedure (likelihood ratio), non-significant variables
were removed from the multivariate model until only
significant (p < 0.05) variables remained. As a result,
loneliness (OR = 1.13, p < 0.001) and mobile phone de-
pendence (OR = 1.07, p < 0.05) were found to be inde-
pendently associated with Internet addiction (Table 3).
A principal finding of this study was a significant pos-
itive correlation between loneliness and Internet addic-
tion. Many studies have reported this finding [6-19],
while Bozoglan et al. [16] and Ceyhan & Ceyhan [19]
also noted that loneliness was the most important vari-
able associated with Internet addiction. In examinations
of the initial hypothesis regarding the relationship be-
tween loneliness and Internet use [8], Internet use was
shown to cause loneliness by isolating individuals from
the real world and deprive them of a sense of a connec-
tion with real-world contacts [8,17]. On the other hand,
Table 1. Mean scores and ranges for examined factors.
Mean ± S.D. Range
Internet Addiction Test (IAT) 38.4 ± 13.5 20 - 87
Mobile Phone Dependence Questionnaire
(MPDQ) 26.6 ± 9.2 6 - 54
UCLA Loneliness Scale 39.7 ± 9.5 22 - 64
Beck Depression Inventory-II (BDI-II) 8.7 ± 8.6 0 - 36
Type A Pattern Scale of Tokai University 38.9 ± 8.7 17.5 - 66.5
Health Practice Index (HPI) 5.5 ± 1.1 3 - 8
Table 2. Subject characteristics and Internet addiction category.
Non-addicts % (n) Addicts % (n)
Male 57.5 (23) 42.5 (17)
Female 56.9 (37) 43.1 (28)
Mode of residence
Solitary 54.3 (50) 45.7 (42)
Other 76.9 (10) 23.1 (3)
Medicine 47.5 (28) 52.5 (31)
Nursing 69.6 (32) 30.4 (14)
Behavior type
Type A 42.3 (11) 57.7 (15)
Type B 62.0 (49) 38.0 (30)
Absent 65.8 (52) 34.2 (27)
Present 30.8 (8) 69.2 (18)
the cognitive-behavioral model for pathological Internet
use (PIU) [42] suggests that loneliness predisposes an
individual to PIU. This latter hypothesis was then con-
firmed by a longitudinal study that did not find a nega-
tive impact of Internet use on loneliness level [11].
Lonely individuals might experience pleasure online
because of the increased potential for companionship and
belonging [9,17]. In this sense, the Internet may provide
an ideal social environment for lonely individuals to in-
teract with other people [17], thus they might be more
prone to Internet addiction.
In the present study, there was a significant positive
correlation between mobile phone dependence and In-
ternet addiction when using a personal computer. Mo-
bile phone dependence and Internet addiction have
common characteristics in regard to two factors; exces-
sive use and an intermittent craving to engage in IT-re-
lated activities. Therefore, it was suggested that students
with higher traits of mobile phone dependence would
show greater levels of Internet addiction. Meanwhile, the
Internet is available for access by mobile phones as well
as personal computers. In particular, smartphones, with
their easy-to-access Internet features, have come into
widespread use in recent years, with a penetration rate in
Japan of about 30%, according to a survey carried out by
Ministry of Internal Affairs and Communications in 2012
[43]. Although we examined the relationship between
mobile phone dependence and Internet addiction when
using personal computers in the present study, additional
examinations are needed to clarify the problem of Inter-
net addiction in individuals using smartphones.
Our multivariate logistic regression analysis results
showed that depressive state was not significantly related
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S. Ezoe, M. Toda / Open Journal of Preventive Medicine 3 (2013) 407-412
Copyright © 2013 SciRes.
Table 3. Univariate and multivariate logistic regression analyses to identify factors associated with Internet addiction.
Univariate analysis Multivariate analysis*
Candidate factors Crude
odds ratio 95% Confidence intervalP valueAdjusted
odds ratio95 % Confidence intervalP value
Gender (1, male; 0, female) 0.98 0.44 - 2.17 0.954
Health-related lifestyle (1, good; 0, poor) 0.82 0.38 - 1.78 0.612
Mode of residence (1, solitary; 0, other) 2.80 0.72 - 10.84 0.136
Department (1, medicine; 0, nursing) 2.53 1.13 - 5.69 0.025
Behavior type (1, Type A; 0, Type B) 2.23 0.91 - 5.48 0.082
Depression (1, present; 0, absent) 4.33 1.67 - 11.25 0.003
Mobile Phone Dependence Questionnaire 1.05 1.00 - 1.10 0.039 1.07 1.01 - 1.12 0.017
UCLA Loneliness Scale 1.12 1.00 - 1.18 0.000 1.13 1.07 - 1.19 0.000
*Using a forward stepwise procedure, non-significant factors were removed from the model until only significant (p < 0.05) factors remained.
to Internet addiction. Casale and Fioravanti reported that
loneliness was a significant predictor of PIU, whereas
depression was not [17], which is consistent with our
findings. Furthermore, Caplan suggested that social
wellbeing (loneliness) played a greater role than psycho-
logical health (depression) in predicting generalized PIU
[44]. Thus, loneliness rather than depression might be
associated with Internet addiction.
Our study has some limitations. The cross-sectional
nature of this examination limits conclusions that can be
reached regarding the causal relationships of Internet
addiction with loneliness and mobile phone dependence.
Also, the effects of extrovert or neurotic personality traits
and other sociodemographic variables, such as academic
achievements and economic condition, were not ana-
lyzed. Furthermore, our questionnaire did not include
items specific to smartphones and, as mentioned above,
further study is needed to examine Internet addiction
with the use of smartphones. In addition, we did not take
into account the types of Internet services accessed by
our subjects, such as e-mail, information, shopping,
games, adult websites, chat rooms, blogging, and SNS.
Finally, the sample size was small and all subjects were
students of the same university, thus a general population
cohort is not represented. Additional examinations with
larger and more varied populations are required.
In summary, our findings suggest that university stu-
dents with feelings of loneliness and mobile phone de-
pendence are prone to have a higher level of Internet
addiction. To identify individuals at high risk of Internet
addiction, it is important to consider those factors.
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