Vol.3, No.1, 99-103 (2013) Open Journal of Preventive Medicine
Multifactorial study of mobile phone dependence in
medical students: Relationship to health-related
lifestyle, Type A behavior, and depressive state
Masahiro Toda1*, Satoko Ezoe2
1Department of Pharmacology, Osaka Dental University, Osaka, Japan; *Corresponding Author: toda-m@cc.osaka-dent.ac.jp
2Shimane University, Health Administration Center Izumo, Shimane, Japan
Received 29 October 2012; revised 3 December 2012; accepted 11 December 2012
We investigated factors contributing to mobile
phone dependence. To 139 medical students, we
administered a self-reporting questionnaire de-
signed to evaluate mobile phone dependence,
health-relate d lifesty l e, pattern s of b eh avior, and
depressive state. Multivariate logistic regression
analysis revealed that scores for poor health-
related lifestyle, Type A behavior pattern, and
presence of depression are independently as-
sociated with degree of mobile phone depend-
ency. These findings suggest that persons with
an unhealthy lifestyle, Type A behavior traits, or
depression might benefit from mo bile phone use
Keywords: Mobile Phone Dependence;
Health-Related Lifestyle; Patterns of Behavior;
Depressive State; Medical Students
In Japan, in March 2011 there were about 120 million
mobile phones in use, which is almost the same as the
total population [1]. Along with the rapid proliferation of
mobile phones, various social issues have arisen, includ-
ing their use in public places and excessive use or even
dependence. View ing problematic mobile phone use as a
type of technostress [2], to gauge mobile phone depend-
ence and identif y high-risk groups, we were quick to d e-
sign the Mobile Phone Dependence Questionnaire (MPDQ)
questionnaire, subsequently confirming its reliability and
validity [3].
In recent studies, we found that mobile phone de-
pendency is associated with unhealthy lifestyle [4,5],
extrovert or neurotic personality traits [5], and maternal
affectionate constraint in childhood [6]. Other factors
that may also contribute to mobile phone dependence
remain to be studied. For example, we think that more
multifactorial research is needed. Consequently, in the
present study, using logistic regression analysis, we ex-
amined associations between mobile phone dependence
and demographic characteristics, health-related lifestyle,
patterns of behavior, and depressive state.
2.1. Subjects
We recruited 139 university students from the Faculty
of Medicine at Shimane University. After the protocol
received the approval of the institute’s review board and
informed consent was obtained from each participant, the
participants filled out a set of self-reporting question-
naires designed to evaluate mobile phone dependence,
health-related lifestyle, behavior pattern, and depressive
state. The answers of the 130 respondents (47 males, 83
females) who properly completed all the questionnaire
items were statistically analyzed. Mean (±SD) age for
males was 19.3 ± 1.8 years and for females 18.7 ± 0.9
2.2. Mobile Phone Dependence
Mobile phone dependence was evaluated using the
MPDQ [3], a self-rating questionnaire which consists of
20 items. Each response is scored on a Likert scale (0, 1,
2, 3). Likert scores for each item are then summed to
provide a quantitative overall mobile phone dependence
score ranging from 0 to 60. Higher scores indicate
greater dependence. Subjects in the highest quartile were
put in the high-dependence category.
2.3. Health-Related Lifestyle
Health-related lifestyle was evaluated using th e Health
Practice Index (HPI) [7,8], in which 1 point is accumu-
lated for each desirable response on 8 items (desirable
criteria are shown in parentheses): smoking habits (not
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M. Toda, S. Ezoe / Open Journal of Preventive Medicine 3 (2013) 99-103
smoking cigarettes), drinking habits (not consuming al-
cohol every day), daily consumption of breakfast (eating
every morning), appropriate daily duration of sleep (7 or
more hours) and work (10 or less hours), regular phy-
sical activity (1 or more times per week), appropriate
levels of subjective stress (low to moderate), and nutri-
tionally balanced diet (eating a nutritionally balanced
diet). Higher scores indicate healthier lifestyle. Respon-
dents with 6 - 8 points were allocated to the good, and
those with 0 - 5 points to the poor, category [9]. Based
upon the lifestyle stud y by Belloc and Breslow [10 ], and
taking into consideration cultural differences, the ques-
tion items, phrasing, and scoring were designed for Ja-
panese subjects.
2.4. Patterns of Behavior
Type A behavior patterns vary according to culture
and nationality [11]. We therefore assessed patterns of
behavior using the Tokai University Type A Pattern
Scale [11,12] designed for Japanese population. The
scale consists of 11 items with total score ranging from
0.25 to 98.75. Subjects were categorized as having either
Type A or Type B behavior patterns: persons with scores
of 43.1 points or more are placed in the Type A category
2.5. Depressive State
Depressive state was assessed using the Beck Depres-
sion Inventory-II (BDI-II) [13], a self-rating question-
naire which consists of 21 items with total score ranging
from 0 to 63. Higher scores indicate greater depression.
The reliability and usefulness of the Japanese version of
the BDI-II have been confirmed [14]. Subjects were
categorized as having either no or minimal, or mild or
greater, depression (cutoff point 13/14) [13].
2.6. Statistical Analysis
Univariate and subsequent multivariate logistic re-
gression analyses were applied to identify possible asso-
ciations between mobile phone dependence and each
factor (demographic characteristics, health-related life-
style, patterns of behavior, and depressive state). All
variables with a p value < 0.2 in the univariate analysis
were included in the multivariate analysis (stepwise
backward elimination) with age adjustment [15]. Statis-
tical significance was set at p < 0.05 and the confidence
interval was 95%.
Table 1 shows scores for each questionnaire. Scores
for mobile phone dependence, mean 26.5, ranged from 6
to 54. Respondents in the highest quartile were put in the
high-depend ence category (cutoff point 33/34).
Table 2 shows personal characteristics related to mo-
bile phone dependence category. Univariate logistic re-
gression results revealed a statistically significant rela-
tionship between mobile phone dependence and
health-related lifestyle (OR = 2.34, p < 0.05), predomi-
nance of Type A behavior traits (OR = 2.49, p < 0.05),
and depressive state (OR = 3.04, p < 0.05) (Table 3). In
addition to these variables, gender and mode of residence
(variables with a p value < 0.2) were included in age-
Table 1. Scores for each questionnaire.
Mean Range SD
Mobile Phone Dependence
Questionnaire (MPDQ) 26.5 6 - 54 9.1
Health Practice Index (HPI) 5.4 3 - 8 1.1
Type A Pattern Scale of Tokai
University 38.4 17.5 - 66.58.7
Beck Depression Inventory-II
(BDI-II) 8.4 0 - 36 8.6
n = 130.
Table 2. Subject characteristics and mobile phone dependence
n (%) High-dependence
n (%)
Male 39 (39.8) 8 (25.0)
Female 59 (60.2) 24 (75.0)
Medicine 58 (59.2) 15 (46.9)
Nursing 40 (40.8) 17 (53.1)
Mode of residence
In a family 14 (14.3) 1 (3.1)
Solitary 84 (85.7) 31 (96.9)
Health-related lifestyle
Good 54 (55.1) 11 (34.4)
Poor 44 (44.9) 21 (65.6)
Patterns of behavior
Type B 79 (80.6) 20 (62.5)
Type A 19 (19.4) 12 (37.5)
Absent 80 (81.6) 19 (59.4)
Present 18 (18.4) 13 (40.6)
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M. Toda, S. Ezoe / Open Journal of Preve nti ve Medicine 3 (2013) 99-103
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Table 3. Univariate and multivariate logistic regression analyses to identify factors associated with high mobile phone dependency.
Univariate analysis Multivariate analysis (final model)*
Candidate factors Crude
odds ratio95% Confidence
interval p value Adjusted
odds ratio 95% Confidence
interval p value
Gender (1, female; 0 , male) 1.98 0.81 - 4.86 0.134
Department (1, nursing; 0, medici ne) 1.64 0.74 - 3.67 0.225
Mode of residence (1, solitary; 0, in a family) 5.17 0.65 - 40.95 0.120
Health-related lifestyle (1, poor; 0, good) 2.34 1.02 - 5.38 0.045 2.51 1.05 - 6.04 0.039
Patterns of behavior (1, Type A; 0, Type B) 2.49 1.04 - 5.98 0.040 2.73 1.04 - 7.19 0.042
Depression (1, pres en t; 0 , absent) 3.04 1.27 - 7 .27 0.012 2.80 1.13 - 6.97 0.027
*Using a backward st epwise procedure, non-significant factors were rem oved from the model until only significant (p < 0.05) fac tors remained.
adjusted multivariate log istic regr ession analysis. Using a
backward stepwise procedure, non-significant variables
were removed from the multivariate model until only
significant (p < 0.05) variables remained. As a result,
poor health-related lifestyle (OR = 2.51, p < 0.05), Type
A behavior (OR = 2.73, p < 0.05), and presence of de-
pression (OR = 2.80, p < 0.05) were found to be inde-
pendently associated with high mobile phone depend-
Meanwhile, univariate logistic regression analysis re-
vealed no significant relationships between particular
lifestyle factors and mobile phone dependence ( Tab le 4).
Moreover, when, in place of health-related lifestyle,
“consumption of breakfast” and “physical activity” (va-
riables with a p value < 0.2) were included in the above-
mentioned multivariate model, in the backward stepwise
procedure both were removed prior to any other factors
(Type A behavior, depressive state, gender, and mode of
Type A individuals are characteristically prone to im-
patience and communicate a sense of urgency about time.
A previous study has also reported that mobile phone
dependence was associated with two facets of the UPPS
Impulsive Behavior Scale, urgency and lack of perse-
verance [16]. Individuals with impetuous and impatient
characteristics may want to make contact with others at
any time and place, which may consequently result in
use of mobile phones in public places even when such
use is considered to be a nuisance. Thus, Type A indi-
viduals may require more intensive guidance about mo-
bile phone use.
It has been reported that excessive mobile phone use is
associated with depression [17,18]. In the present study,
we also found a significant relationship between mobile
phone dependence and depressive state. These findings
suggest that, for depressed individuals, mobile phones
may be a stress-coping tool. By mobile phone, they may
be able to ask others’ advice about their distress and fur-
thermore express their feelings better than by directly
face-to-face communication. This hypothesis is supported
by previous studies which suggested that mobile phones
may increase social support [19,20]. Unfortunately, how-
ever, because this study was cross-sectional, we cannot
conclusively establish causality. Longitudinal studies are
required. Incidentally, in a previous study, we found no
significant relationship between mobile phone depend-
ence and depressive state evaluated using the Zung
Self-Rating Depression Scale (Zung-SDS) [5]. This find-
ing may be due to different depressive state scoring cri-
teria in BDI-II and Zung-SDS. This may be clarified in a
future study that uses these two instruments at the same
Consistent with our previous findings [4,5], there was
a significant relationship between mobile phone depend-
ence and comprehensive health-related lifestyle. These
findings suggest that people who excessively use mobile
phones may benefit from more comprehensive guidance,
including how to follow a healthier lifestyle. Other pre-
vious studies have also suggested that excessive mobile
phone use may be associated with poor lifestyle habits,
such as smoking or daily alcohol consumption [17,21].
In the present study, however, no significant relation-
ships were found between particular lifestyle factors and
mobile phone dependence. This may have been due to
the inclusion of proportionally fewer individuals with
detrimental smoking or drinking habits. Larger popula-
tions are needed fo r future studies.
This research has several limitations. As already men-
tioned in the discussion, this study was cross-sectional,
and the sample size was too small to provide conclusive
results. Furthermore, our questionnaire did not include
items specific to smartphones. In recent years, smart-
phones have rapidly come into widespread use, and, ac-
cording to a survey carried out by comScore, Inc. (2012),
in June 2012, the penetration rate in Japan is over 20%
M. Toda, S. Ezoe / Open Journal of Preventive Medicine 3 (2013) 99-103
Table 4. Percentage of respondents with poor health-related lifestyle and results of univariate logistic regression analysis to identify
factors associated with high mobile phone dependency.
Health-related lifestyle factor s
(1, good; 0, poor) Low-dependence
n (%) High-dependence
n (%) Crude odds ratio95% Confidence interval p value
Smoking habits 0 (0) 2 (6.3) NC* - -
Drinking habits 0 (0) 1 (3.1) NC* - -
Consumption of breakfast 14 (14.3) 8 (25.0) 0.50 0.19 - 1.33 0.166
Duration of sleep 75 (76.5) 23 (71.9) 1.28 0.52 - 3.14 0.596
Duration of work 43 (43.9) 14 (43.8) 1. 01 0.45 - 2.25 0 .990
Physical activity 10 (10.2) 7 (21.9) 0.41 0.14 - 1.18 0.096
Subjective stress 26 (26.5) 12 (37.5) 0.60 0.26 - 1.40 0.239
Nutritional balance 74 (75.5) 23 (71.9) 1.21 0.49 - 2.96 0.682
*Odds ratio not calculable b ecause of zer o v alue.
[22]. Smartphones are more like tablet computers than
mobile phones, and therefore may herald another change
in the way mobile telecommunication are used. Based on
recent developments, we are planning further studies.
In conclusion, the major finding of this study is that,
when adjusted for other factors, poor health-related life-
style, Type A behavior, or presence of depression may be
statistically significantly associated with high mobile
phone dependency. Persons with an unhealthy lifestyle,
Type A behavior traits, or depression might benefit from
mobile phone use guidance.
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