How technology addiction affects social anxiety in adolescent girls?

Introduction

Adolescence period, in which physical and psycho-social changes

occur abruptly and rapidly and personality traits develop on a large

scale, is accepted to be a very important and critical period in terms

of development. It is indicated that the most common psychiatric

disorders among mental health problems encountered in this

period are anxiety disorders and depression. An adolescent, who

refuses to have actively face-to-face communication with persons

such as her/his friends or parents in conditions such as anxiety and

introversion, may use the technology more .

Technology addiction is caused by a number of factors and it is

possible to assert that adolescents’ needs which periodically arise

depending on their developmental characteristics are among the

most important reasons of addiction . In a study in which the data were collected

from 337 students to measure the effects of excessive internet and

phone use on health , it was found that intense internet use was

related with high anxiety [5]. It was also concluded that excessive

phone use was associated with sleeplessness and also high

anxiety. Many studies have revealed that technology use gradually

increases today and becomes widespread especially among the

youth . Misuse of technology may reduce social relations and

cause loneliness and anxiety in adolescents.

Social anxiety is characterized by shaking of hands, sweating,

blushing and fearing to feel humiliated while sitting and talking

in a community . Just like many other anxiety disorders, social

phobia frequently starts in childhood and adolescence period. In

their study, revealed that initial symptoms of social

phobia usually started around the age of 15.5 on average . Most

studies on this subject have shown that average onset age coincides

with adolescence. The studies have shown that social anxiety is a

common condition in adolescence period and is accompanied by

many problems .

This original study is expected to contribute

to psychiatric nursing in terms of approaches and applications for

individuals, who are affected by technology addiction and social

anxiety. The purpose of this study was to determine the howtechnology addiction affects social anxiety in adolescent girls.

In the study, an answer was sought to the following question:

Is there a correlation between technology addiction and social

anxiety in adolescent girls?

Population and sample of the study

The population of this study consisted of 2052 female students

receiving education in five high schools, which were determined

among 22 high schools by lot according

to number of students. The sample of the study consisted of 635

students who were determined by power analysis with 0.05 level

of significance, confidence interval of 0.95, effect size of 0.3 and

ability to represent population of 0.95. While selecting the samples

from the population, students were listed according to their school

numbers and determined using simple random sampling method.

Data collection tools

The data were collected using the descriptive characteristics

form, technology addiction scale, and social anxiety scale for

adolescents.

Descriptive characteristics form

The descriptive characteristics

form includes a total of six questions about socio-demographic

characteristics of adolescents (age, mother’s and father’s

educational level, mother’s and father’s occupation, and perception

of income status.

Technology addiction scale

Social Network Addiction

Subscale (SNAS), Instant Messaging Addiction Subscale (IMAS),

Online Gaming Addiction Subscale (OGAS), and Web Site

Addiction Subscale (WSAS). The Cronbach’s Alpha coefficients

for the subscales were calculated as 0.806 for IMAS, 0.786 for

SNAS, 0.897 for OGAS, and 0.861 for WSAS, respectively [2].

According to the data acquired from the sample group of this study,

the Cronbach’s Alpha internal consistency reliability coefficient

was calculated as 0.93 for the overall scale, 0.79 for social networkuse, 0.80 for instant messaging, 0.86 for online gaming, and 0.88

for web site use.

Data analysis

SPSS) 22.0 program was used to analyze the data. In assessment

of the data, percentage distribution to compare descriptive

characteristics of the adolescents, mean to compare mean scores

of the scales, independent samples t-test to compare age groups,

gender and mean scores of the scales, analysis of variance to

compare mother’s and father’s educational level, mother’s and

father’s occupation, level of income and mean scores of the scales, advanced analysis to evaluate the difference between the groups and

correlation analysis to determine how the scales affected each other

were used. In the study, p˂0.05 was considered as significant.

Results

In the study, it was observed that ages of 35.9% of the adolescents

ranged from 13 to 15 years, 64.1% ranged from 16 to 18 years,

mothers of 33.5% were primary school graduate, fathers of

28.7% were secondary school graduate, mothers of 92.1% were

housewife, fathers of 48.7% had self-employed, and 90.6% had a

good level of income (Table 1).

It was found that mean scores of the adolescents were 12.82±5.7

for social network addiction, 13.76±6.14 for instant messaging,

10.31±5.66 for online gaming, and 13.61±6.86 for web site

addiction. Total mean score of technology addiction was

50.52±20.47. Considering total mean score of the scale, it was

determined that technology addiction of adolescents was moderate.

When comparing the technology addiction subscale and total mean

scores of the adolescent girls with father’s occupation, it was seen

that there was a statistically significant difference between web site

addiction subscale and total mean scores and father’s occupation

(p<.05). This difference between the groups was caused by those

whose father was unemployed. When comparing the technology

addiction subscale and total mean scores of the adolescent girls

with their perception of income status, it was observed that there

was a statistically significant difference between social network addiction subscale and perception of income status (p<.05). Those

with a higher level of income had a higher level of social network use.

When comparing descriptive characteristics of the adolescent girls

and their total mean scores of the Social Anxiety Scale, it was

found that there was no statistically significant difference between

age groups and total scores of the Social Anxiety Scale (p>.05).

The difference between mother’s educational level and total scores

of the Social Anxiety Scale was found to be statistically significant

(p<.05). Those, whose mother was not literate, had a higher level

of social anxiety. It was determined that there was a statistically

significant difference between father’s educational level and total

scores of the Social Anxiety Scale (p<.05). Those, whose father

was primary school graduate, had a higher level of social anxiety.

The difference between mother’s and father’s occupation and total

scores of the social anxiety scale was not statistically significant

(p>.05). The difference between perception of income status and

total scores of the social anxiety scale was statistically significant

(p<.05). Those, who had a bad perception of income status, had a

higher level of social anxiety .

A positive correlation was observed between adolescents’ subscale

and total mean scores of the Technology Addiction Scale and total

mean scores of the Social Anxiety Scale (p<.05). In the study, it

can be said that as social anxiety increases, technology addiction

increases, or as technology addiction increases, social anxiety also increases.

Conclusion and recommendation

In this study conducted to determine the correlation between

technology addiction and social anxiety in adolescent girls, it was

found that technology addiction and social anxiety of adolescent

girls were moderate and as their technology addiction increased,

their social anxiety also increased. It can be recommended to

provide trainings to parents and adolescents for the proper use

of technology in adolescents, provide necessary trainings in

schools explaining advantages, disadvantages and proper use of

technology, arrange social activities in schools and at home to

keep the youth away from technology, conduct studies measuring

social anxiety and technology addiction levels of students with the

cooperation of counseling units in schools and community mental

health centers and take necessary precautions, and conduct similar

studies examining the correlation between technology addiction

and social anxiety level in adolescents with larger sample groups.