Multifactorial study of mobile phone dependence in medical students: Relationship to health-related lifestyle, Type A behavior, and depressive state

doi:10.4236/ojpm.2013.31012

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Vol.3, No.1, 99-103 (2013) Open Journal of Preventive Medicine

http://dx.doi.org/10.4236/ojpm.2013.31012

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: [email protected]

2Shimane University, Health Administration Center Izumo, Shimane, Japan

Received 29 October 2012; revised 3 December 2012; accepted 11 December 2012

ABSTRACT

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

guidance.

Keywords: Mobile Phone Dependence;

Health-Related Lifestyle; Patterns of Behavior;

Depressive State; Medical Students

1. INTRODUCTION

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. MATERIALS AND METHODS

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

years.

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

M. Toda, S. Ezoe / Open Journal of Preventive Medicine 3 (2013) 99-103

100

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

[11].

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%.

3. RESULTS

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

category.

Low-dependence

n (%) High-dependence

n (%)

Gender

Male 39 (39.8) 8 (25.0)

Female 59 (60.2) 24 (75.0)

Department

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)

Depression

Absent 80 (81.6) 19 (59.4)

Present 18 (18.4) 13 (40.6)

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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-

ency.

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

residence).

4. DISCUSSION

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

time.

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%

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102

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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