Relationships of loneliness and mobile phone dependence with Internet addiction in Japanese medical students

doi:10.4236/ojpm.2013.36055

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Vol.3, No.6, 407-412 (2013) Open Journal of Preventiv e Me dic ine

http://dx.doi.org/10.4236/ojpm.2013.36055

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: [email protected]

2Department of Pharmacology, Osaka Dental University, Osaka, Japan

Received 25 July 2013; revised 12 August 2013; accepted 19 August 2013

License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

ABSTRACT

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

students.

Keywords: Internet Addiction; Mobile Phone

Dependence; Loneliness ; Depression; Medical

Students

1. INTRODUCTION

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-

ence.

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

activities.

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

S. Ezoe, M. Toda / Open Journal of Preventive Medicine 3 (2013) 407-412

408

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

addiction.

2. MATERIALS AND METHODS

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,

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%.

3. RESULTS

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).

4. DISCUSSION

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)

Gender

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)

Department

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)

Depression

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

S. Ezoe, M. Toda / Open Journal of Preventive Medicine 3 (2013) 407-412

410

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.

OPEN A CCESS

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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