Psychiatria Danubina, 2017; Vol. 29, No.

2, pp 207-213 Original paper

© Medicinska naklada - Zagreb, Croatia

MEDIATOR EFFECTS OF POSITIVE EMOTIONS ON SOCIAL

SUPPORT AND DEPRESSION AMONG ADOLESCENTS SUFFERING
FROM MOBILE PHONE ADDICTION
Menglong Li1, Xia Jiang2 & Yujia Ren1
1
Physical Education Institute, Hunan First Normal University, Changsha, China
2
Wuhan Sports University, Wuhan, China

received: 26.9.2016; revised: 20.2.2017; accepted: 14.3.2017

SUMMARY
Background: Depression is a common mental disorder that is widely seen among adolescents suffering from mobile phone
addiction. While it is well known that both positive emtions in adolescents wiotions and social support can have a positive impact by
helping individuals to maintain a positive attitude, the correlation between positive emotions, social support, and depression among
these adolescents remains to be investigated. This study examined the mediator effects of positive emotions on the relationship
between social support and depression among adolescents suffering from mobile phone addiction.
Subjects and methods: For this study, conducted in 2016, we selected 1,346 adolescent students from three middle schools
(ranging from Junior Grade One to Senior Grade Three) in Hunan Province of China, to participate in the survey. Participants were
selected using the stratified cluster random sampling method, and all participants remained anonymous throughout the study. Each
participant completed the Self-made General Situation Questionnaire, the Social Support Rating Scale, the Positive and Negative
Affect Schedule, the Center for Epidemiological Studies Depression Scale, and the Mobile Phone Addiction Tendency Scale.
Results: There was significant positive correlation between positive emotions and social support. Both positive emotions and
social support demonstrated significant negative correlation with depression. Positive emotions had partial mediator effects on the
relationship between social support and depression (P<0.01).
Conclusions: Both social support and positive emotions can lower levels of depression among adolescents suffering from mobile
phone addiction. Social support contributes to positive emoth mobile phone addiction, thereby reducing their levels of depression.
These findings suggest that more support and care should be given to this particular adolescent population.

Key words: depression - mobile phone addiction – adolescents - mood disorder - positive emotions - social support

* * * * *

INTRODUCTION have mobile phone addiction to non-addicted students,

Chen et al. (2017) detected reduction in the functional
In recent years, products based on advances in connectivity of the bilateral hippocampus and bilateral
electronic technology have played an increasingly frontal lobe among the addicted students. The changes
important role in daily life. Consequently, excessive use of these structures were correlated with emotional
of technology-based goods and services can leads to problems, impulsive behavior, low self-esteem, and the
dependence on, or even addiction to, these products, quality of sleep.
such as Internet Addiction and Mobile Phone Addiction Although mobile phone addiction is not the same as
(Chen et al. 2016). Mobile phone addiction is a rela- Internet addiction, they both belong to the general cate-
tively new phenomenon of human behavior that can gory of addiction to high-tech electronic products, and
impact users who have developed an extreme reliance both are a type of behavioral addiction. Previous related
on their phones for any reason. Users who have deve- studies have demonstrated significant positive corre-
loped mobile phone addiction exhibit behavior characte- lations between Internet addiction and anxiety, depres-
rized by damaged psychological and social functions sion, loneliness, and negative emotions. In addition,
(Billieux et al. 2015). Because of the convenience and research has shown that normal use of the commu-
capabilities of mobile phones, an increasing number of nication functions of the Internet can help alleviate
adolescents have been found to suffer mobile phone symptoms of depression, whereas use of other non-
addiction. Mobile phone addiction brings many notice- communication functions of the Internet will aggravate
ably negative effects to adolescents, such as inducing symptoms of depression among individuals (Su et al.
physiological diseases, affecting rest or sleep, impacting 2011, Meekyung 2014). Because of the similarities bet-
interpersonal relationships, reducing classroom learning ween these two types of addiction, studies of mobile
efficiency, increasing economic pressure, and reducing phone addiction can learn from research on Internet
comprehension ability. Mobile phone addiction can also addiction.
lead to personality disorders (Montag et al. 2015, Zhou There are many factors influencing mobile phone
2013). In a study that compared college students who addiction in adolescents. Studies have revealed that lack

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AMONG ADOLESCENTS SUFFERING FROM MOBILE PHONE ADDICTION Psychiatria Danubina, 2017; Vol. 29, No. 2, pp 207-213

of social support may be one of the main factors leading Hunan Province, China, to participate in the survey.
to behavioral addiction among adolescent students Participants were selected using the stratified cluster
(Ruan et al. 2011). The interpersonal relationship theory random sampling method. All participants were bet-
model argues that when faced with stressful life events, ween thirteen and seventeen years old. To protect the
individuals who receive lower levels of social support privacy of these students, they remained anonymous
are more prone to depression. Social support refers to throughout the study. For the survey, 1,302 valid copies
various functions provided by a social network to of the questionnaire were returned, with a recovery rate
improve an individual’s mental health or reduce his/her of 96.7%, from 690 boys (accounting for 53.0%) and
psychological problems (Theodoritsi et al. 2016). Some 612 girls (accounting for 47.0%). Of the total group,
researchers have proposed that social support can 645 participants were only children (accounting for
reduce adolescent depression, and good social support 49.5%), and 657 participants had siblings (accounting
can be effective in alleviating psychological pressure, for 50.5%). Further, 674 students came from cities and
promoting mental health, enhancing social adaptability, towns (accounting for 51.8%), while 628 students were
and improving the quality of life (Vyavaharkar et al. from rural areas (accounting for 48.2%). Consent for the
2011). Nonetheless, the moderate degree of correlation survey was obtained from the students, their parents,
between social support and depression shows that they their teachers, and their school leaders. Participation by
are not directly correlated. A thorough understanding of all subjects was voluntary.
the mediator effects of social support on depression is
conducive to the development of strategies for targeted Instruments
intervention and prevention of depression.
As the diagnostic criteria of depression indicate, de- The questionnaire gathered general information
pression is closely related to the loss of positive emo- about the research subjects (including gender, age,
tions. Positive emotions are the emotions marked by origin place, and whether the one-child) and four rating
happy feelings (Luo 2012, Truţă & Cazan 2015). scales were employed.
Research has shown that positive emotions can restore ƒ Social Support Rating Scale (SSRS): The Social
the state of various types of cardiovascular activity Support Rating Scale, designed by Xiao (1994), had
previously reduced by negative emotions, bringing 10 items, including three dimensions: objective
cardiovascular activity back to the normal baseline support (3 items), subjective support (4 items), and
(Chiew 2014). At the same time, studies have suggested the degree of use of social support (3 items). SSRS
that fewer negative emotions are observed in partici- generally uses a multi-axis evaluation method for
pants with higher levels of positive emotions, and the rating. Because of the scale’s reasonable design, it is
lack of positive emotions can be followed by the easy to understand the items correctly. The test-
appearance of depression (MacKenzie 2015). According retest reliability was 0.92, with the consistency of
to the “shock absorber” model of social support, social each item between 0.89 and 0.94. Undoubtedly, this
support can improve an individual’s positive emotions scale was well able to reflect participants’ levels of
and absorb the negative effects of stress, highlighting social support because of its good reliability and
the role of positive emotions in the process of dealing validity. In this measurement, the Cronbach alpha
with depression. Individuals who have good social coefficient was 0.72.
support have higher levels of positive emotions and ƒ Positive and Negative Affect Schedule (PANAS): The
lower levels of negative emotions (Lyubomirsky & Positive and Negative Affect Schedule (PANAS),
Layous 2013). developed by Watson et al. (1988), contained two
To sum up, social support and positive emotions parts, including a total of 20 questions about positive
have positive effects on depression, and there is certain emotions and negative emotions. Positive emotions
correlation between social support and positive emo- were covered by Questions 1, 3, 5, 9, 10, 12, 14, 16,
tions. Past research has explored the proposal that social 17 and 19, while negative emotions were covered by
support can affect depression either directly or through Questions 2, 4, 6, 7, 8, 11, 13, 15, 18 and 20. The
the mediator role of other variables. Accordingly, this five-point Likert scale was used for scoring, ranging
study was designed to examine the mediator effects of from “almost no” =1 to “very much” =5. The
positive emotions on the relationship between social homogeneity degree of the two subscales was 0.85
support and depression among adolescents suffering for positive emotions and 0.83 for negative emo-
mobile phone addiction. tions. The test-retest reliability of the two subscales
was 0.47 for positive emotions and 0.47 for negative
emotions. In terms of structure validity, the varimax
SUBJECTS AND METHODS
rotation method was utilized to determine the load
from each factor. The load of each item on positive
Participants
emotions was between 0.76 and 0.40, with an
For this study, conducted in 2016, we selected 1,346 average load of 0.65. The load of each item on
adolescent students from three middle schools (ranging negative emotions was between 0.75 and 0.45, with
from Junior Grade One to Senior Grade Three) in an average load of 0.62.

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AMONG ADOLESCENTS SUFFERING FROM MOBILE PHONE ADDICTION Psychiatria Danubina, 2017; Vol. 29, No. 2, pp 207-213

ƒ Center for Epidemiological Studies Depression nation of the purpose of the test, along with clarification
Scale (CES-D): The Center for Epidemiological of all corresponding requirements. The participants
Studies Depression Scale (CES-D) was designed were required to fill in the questionnaire independently
especially for evaluating the frequency of current within 30 minutes and return it on the spot.
symptoms of depression (Devins & Orme 1985).
The scale focused on depressive emotions, aiming to Statistical Analysis
compare the investigation results of different time
sections. The scale, a type of self-reported measu- All data were processed and analyzed statistically
ring tool, was composed of 20 items in total, with using SPSS 17.0. An independent sample test was
each item scored at four levels: “occasionally or no,” employed to provide a comparison between the two
“sometimes,” “often or half the time,” and “most of groups of quantitative data. Pearson correlation analysis
the time, or continuously.” The total possible score was utilized for correlating the analyses of the two
ranged from 0 to 60 points. A higher score indicated variables, and a chi-square test was used to compare the
higher frequency of the appearance of depression. two groups of qualitative data. A path analysis model
The Cronbach α coefficient and Spearman-Brown was used to analyze the mediator effects of positive
coefficient were both above 0.90. The test-retest reli- emotions on social support and depression among
ability within 12 months was 0.32; the test-retest adolescents suffering mobile phone addiction, with
reliability within 4 weeks was 0.67. In this measu- P<0.05 to indicate that the difference was statistically
rement, the α coefficient was 0.87. significant.
ƒ Mobile Phone Addiction Tendency Scale (MPATS):
The MPATS scale, developed by Xiong et al. (2012), RESULTS
included a total of 16 items in four dimensions:
withdrawal, salience, social comfort, and mood alte- Detection Rate and Related Factors
ration. “Withdrawal” referred to the physiological or of Mobile Phone Addiction
psychological negative reaction when the user was
In our study, users who scored three points or above
not participating in mobile phone related activities.
on the Mobile Phone Addiction Tendency Scale were
“Salience” implied that the use of mobile phones
defined as mobile phone addicts. Among the 1,302
dominated the center of thinking and activities.
subjects, a total of 354 participants were found to be
“Social comfort” referred to the role played by
mobile phone addicts, with a detection rate of 27.1%
mobile phone use in interpersonal communication.
(see Table 1). Gender, origin place, and status as an
“Mood alteration” referred to a change of mood
only child were not associated statistically with the
caused by the use of mobile phones. A scale from 1-
detection rate of mobile phone addiction (P>0.05).
5 was used for scoring, ranging from “extremely not
accordant” to “very accordant.” Mobile phone
addiction tendency and each dimension were scored Comparison of Scores in Social Support,
based on the average score of their items. A higher Positive Emotions, and Depression between
score indicated a higher tendency toward mobile Mobile Phone Addicts and Non-addicts
phone addiction. Respondents who scored above
Comparison of scores for social support, positive
three points were defined as mobile phone addicts.
emotions, and depression between mobile phone addicts
In this study, the Cronbach α coefficient of each
and non-addicts showed that mobile phone addicts sco-
dimension was 0.79-0.86; the Cronbach α coefficient
red lower in social support and positive emotions than
of the entire scale was 0.90; the correlation between
non-addicts, but scored higher in depression than non-
each item and the scale was 0.66-0.85.
addicts, with differences that are all statistically signi-
For this study, each of the classes underwent group ficant (P<0.05). Social support was manifested mainly
testing. Prior to distributing the test, we provided unif- as subjective and objective support (see Table 2).
orm instructions accompanied by an introductory expla-

Table 1. Comparison of the detection rate of mobile phone addiction between different subjects
Total No. No. of mobile phone addicts Detection rate (%) χ2 P
Gender M 690 174 25.22 2.882 0.090
F 612 180 29.41
Origin place Urban 674 189 28.04 0.513 0.474
Rural 628 165 26.27
Whether an Y 645 188 29.15 2.476 0.116
only child N 657 166 25.27
Total 1302 354 27.19

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Table 2. Comparison of scores in social support, positive emotions and depression between mobile phone addicts and
non-addicts
Total score in Subjective Objective Degree of Positive Score in
Group
social support support support using support emotions depression
Addicts 35.75±4.98 14.79±3.25 13.59±2.22 7.37±1.32 2.69±0.66 15.03±3.18
Non-addicts 37.71±4.18 15.71±3.02 14.52±2.38 7.48±1.12 3.02±0.71 13.04±3.58
t 7.133 4.789 6.387 1.500 7.604 9.191
P <0.001 <0.001 <0.001 0.134 <0.001 <0.001

Table 3. Correlation analysis of scores in social support, positive emotions and depression
Subjects Social support Positive emotions Depression
Social support 1
Mobile phone addicts Positive emotions 0.306* 1
Depression -0.303* -0.359* 1
* represents P<0.05

Correlation Analysis of Scores in Social DISCUSSION

Support, Positive emotions and Depression
Among the 1,302 adolescent students surveyed in
Pearson correlation analysis was used to analyze
this study, a total of 354 students were determined to be
scores for social support, positive emotions, and de-
mobile phone addicts, with a detection rate of mobile
pression. The total score in social support of adolescents
phone addiction of 27.1%. The detection rate of mobile
suffering mobile phone addiction was correlated
phone addiction in this study was lower than the rate
positively with positive emotions (r=0.306), while the
achieved by Ge et al. (2014). Their research surveyed a
score of these subjects in depression was correlated
total of 1,200 adolescents in school, used the same mea-
positively with social support and positive emotions
suring tools, and obtained a detection rate of 30.78% for
(r=0.303; r=0.359) (see Table 3).
mobile phone addiction. There are several possible
reasons for the difference between their results and ours.
Analysis of Mediator Effect First, the inconsistency might be associated with the
The model of was constructed based on the theory of sample size, since our survey involved a larger number
Introduction. In this model, social support is an of participants. Second, the subjects of our study
exogenous observation variable; depression is an included both Junior Middle School students and Senior
endogenous variable; positive emotions are the mediator High School students. However, the subjects of the
variable. The path analysis demonstrated that among research by Ge et al. (2014) were secondary and higher
adolescents suffering mobile phone addiction, positive vocational students. Whether there is homogeneity
emotions play a partial mediator role in the relationship between these two samples needs further research.
between social support and depression. In other words, Third, the tool used by Ge et al. (2014) to measure
social support can reduce depression directly, and social mobile phone addiction was different from the one used
support can mitigate depression through positive in our study. Their mobile phone addiction index scale
emotions (see Figure 1). was developed by Leung (2008), and included a total of
17 items in four dimensions: out of control, withdrawal,
escape, and inefficiency. It is common knowledge that
inconsistency between different research results can be
related to the nature of the sample, sample size, test
tools, test environment, and other factors. Future
research should employ standardized research processes
and comparable research tools to acquire rigorous
research results (Du et al. 2014).
Our study also found a significant difference in the
detection rate of mobile phone addiction between stu-
dents of liberal arts as compared to students of science.
Specifically, the detection rate was significantly higher
Note: * represents P<0.05; ** represents P<0.01. among students of liberal arts than among students of
science, but there was no significant difference in
Figure 1. Mediator effects of positive emotions on gender, origin place, or status as an only-child. This
social support and depression among adolescents result reveals a certain universality and a certain
suffering mobile phone addiction particularity of mobile phone addiction in adolescents.

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Students of liberal arts and students of science deal with revealed a negative correlation between depression and
different learning content and different distributions of positive emotions. With the rise of positive psychology,
learning time. Research (Yan & Chen 2016) shows that more and more attention has been paid to the effects of
liberal arts students have relatively lower motivation positive emotions on depression (Carl et al. 2013,
and less interest in learning than science students when Greening et al. 2014).
they are not studying. Therefore, students of liberal arts Positive emotions are an important resource for
are likely to be more obsessed with mobile phones, regulating internal psychological activities. With the
resulting in a higher level of mobile phone addiction. help of this resource, individuals are more likely to
Regarding the total scores of mobile phone addicts believe that they are loved, that others are concerned
in the area of social support, as well as the two about them, and they are being respected. Furthermore,
dimensions of objective and subjective support, this social support is an important source of positive
study found that their scores in positive emotions were emotions (Yang et al. 2016). For example, intervention
significantly lower than non-addicts. There was no studies of female drug addicts have demonstrated that
significant difference between addicts and non-addicts after a period of receiving ongoing social support (inclu-
in the degree of use of social support, but addicts’ ding objective support and subjective support), female
scores in depression were significantly higher than non- drug addicts have been found to experience significantly
addicts. From the results of previous Internet addiction- higher positive emotions. This perception leads to a
related research, it can be shown that Internet addicts more firm belief to abandon drug habits, which in turn
have a lower level of mental health, with their depres- results in more successful detoxification. The consi-
sion levels significantly higher than non-Internet stency between the results of those studies and our cur-
addicts, which is consistent with the findings of this rent research further illustrates the positive correlation
study (Gou et al. 2013). between social support and positive emotions.
According to the interpersonal relationship theory The path analysis of this study found that among
model, when individuals are faced with stressful life adolescents suffering mobile phone addiction, positive
events, if the support gained from social networks is not emotions play a partial mediator role in the relationship
sufficient to help them weather these events, they are between social support and depression. In other words,
more likely to seek virtual psychological support through social support directly affects depression, and social
other means, such as the Internet or mobile phones. support also affects depression by means of positive
Therefore, it can be demonstrated that there is a nega- emotions. Ge et al. (2014) explored the proposed idea
tive correlation between social support and mobile that social support has a direct effect on mobile phone
phone addiction (Du et al. 2014). Meanwhile, when addiction in adolescents. They found that adolescents
individuals, either mobile phone addicts or non-addicts, suffering from lack of social support had a higher
need to get more social support, they will have a higher probability of seeking social support from phones or the
incentive to look for a source of support, as well as virtual network space, and the psychological satisfaction
more enthusiasm for maximizing their use of existing they received from the virtual space could, in turn,
social support. Thus, in the dimension of the degree of enhance their dependence on mobile phones. Yang et al.
using support, there might not be a significant (2016) conducted a meta-analysis of 92 articles from
difference between mobile phone addicts and non- CNKI, VIP, Wanfang Data, and other foreign databases.
addicts. These reports treated Chinese college students as the
Our finding that the scores of mobile phone addicts research subjects, and viewed social support and de-
for positive emotions were significantly lower than the pression as the main research variables. Their findings
scores of non-addicts was closely related to the addicts’ suggested the existence of a stable, moderate negative
perceptions of their social support. Following the correlation between Chinese college students’ social
analysis above, in the face of stressful life events, if support and depression. Furthermore, it has been noti-
individuals perceive that they have more social support, ced that there is a high level of similarity in the types
they have more positive emotional experiences. Positive and amounts of social support provided by individuals
emotions can play a buffer role in negative stressful life living in the same culture. Therefore, it likely that the
events, thus reducing the probability of dependence on relationship between social support and depression
mobile phones and the onset of depression (Wei et al. among adolescent mobile phone addicts is basically the
2014). same across China (Taylor et al. 2004). In addition, the
The results of this study demonstrated that the total research by Yang et al. (2016) demonstrated that there is
score for social support of adolescents suffering mobile a moderate correlation between social support and
phone addiction was positively correlated with positive depression, which leaves open the possibility of other
emotions, and their scores in depression were negatively mediators or regulating variables.
correlated with social support and positive emotions. Combining the theoretical analysis above and
The relationship between depression and positive statistical results of the research data, it is possible to
emotions is self-evident: the development of depression conclude that social support can affect depression either
is often accompanied by reduction in positive emotions directly or through positive emotions. Research has
(Carl et al. 2014). A substantial number of studies have shown that perceived social support has a closer corre-

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AMONG ADOLESCENTS SUFFERING FROM MOBILE PHONE ADDICTION Psychiatria Danubina, 2017; Vol. 29, No. 2, pp 207-213

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Correspondence:
Menglong Li, PhD, Associate Professor
Physical Education Institute, Hunan First Normal University
The Third Fenglin Road, Changsha City, 410205, Hunan Province, China
E-mail: lml0713@yeah.net

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