Fatigue: Biomedicine, Health & Behavior
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/rftg20
Effect of technology addiction on academic
success and fatigue among Turkish university
students
Havva Sert, Feride Taskin Yilmaz, Azime Karakoc Kumsar & Dilek Aygin
To cite this article: Havva Sert, Feride Taskin Yilmaz, Azime Karakoc Kumsar & Dilek
Aygin (2019) Effect of technology addiction on academic success and fatigue among
Turkish university students, Fatigue: Biomedicine, Health & Behavior, 7:1, 41-51, DOI:
10.1080/21641846.2019.1585598
To link to this article: https://doi.org/10.1080/21641846.2019.1585598
Published online: 26 Feb 2019.
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FATIGUE: BIOMEDICINE, HEALTH & BEHAVIOR
2019, VOL. 7, NO. 1, 41–51
https://doi.org/10.1080/21641846.2019.1585598
Effect of technology addiction on academic success and
fatigue among Turkish university students*
Havva Serta, Feride Taskin Yilmazb, Azime Karakoc Kumsarc and Dilek Aygina
a
Faculty of Health Science, Nursing Department, Sakarya University, Sakarya, Turkey; bSchool of Susehri
Health High, Nursing Department, Sivas Cumhuriyet University, Sivas, Turkey; cBiruni University, Faculty of
Health Science, Nursing Department, Istanbul, Turkey
ABSTRACT
ARTICLE HISTORY
Background: Technology addiction can cause certain physical,
mental, and social health problems.
Purpose: This study was conducted to determine the effect of
technology addiction levels on academic success and fatigue in
university students in Turkey.
Methods: 743 students continuing their undergraduate education
at a single university participated in this descriptive correlational
study. Data was collected using a Student Identification Form, The
Problematic Mobile Phone Use Scale, the Internet Addiction Scale,
and the Piper Fatigue Scale.
Results: 9.8% of the students exhibited internet addiction risk, while
internet addiction was detected in 0.7%. Compared to students
showing no addiction symptoms, students who scored in the
internet addicts’ category were found to have lower academic
success averages and higher fatigue levels. It was found that
smart phone addiction of students alone explained 5.8% of the
total variance in fatigue levels while the internet addiction of
students alone explained 6.8% of the total variance in fatigue levels.
Conclusion: Although internet addiction was relatively low in this
study, academic success was negatively affected in students
categorized as internet addicted and fatigue increased alongside
technology addiction, suggesting that internet addiction may be a
predictor of fatigue. Educational initiatives could help to raise
awareness on the negative relationship between technology
addiction and academic success and its effects on physical and
psychological health.
Received 28 December 2018
Accepted 12 February 2019
KEYWORDS
University students; smart
phone; internet; addiction;
fatigue; Turkey
Introduction
Today, communication technologies such as the smart phone and the internet have started to
seriously impact the daily lives of individuals [1]. Independent of time and location, it is possible to obtain information from databases and libraries worldwide, communicate with other
people, receive education, listen to music, watch movies, play games, shop, and fulfill many
other individual needs such as benefiting from banking services through smart phones and
the internet [2,3]. For these reasons, a large increase in internet use has been observed in
CONTACT Feride Taskin Yilmaz
feride_taskin@hotmail.com
*This article was presented poster at the 19th National Internal Medicine Congress of organized between October 11–15,
2017 in Antalya, Turkey.
© 2019 IACFS/ME
42
F. TASKIN YILMAZ
Turkey in recent years as with the rest of the world. For instance, the rate of internet use
among individuals in the 16–74 age group was 61.2% in the year 2016, but increased to
66.8% in 2017, according to data from the ‘Household Information Technologies Use’
survey conducted by the Turkish Institute of Statistics [4]. In two studies conducted in 2010
and 2018, the prevalence of internet addiction in students increased from 14% to 17.7% [5,6].
While technology makes life easier and positively contributes to social development and
modernization, it also has led to the emergence of new behavioral problems, such as smart
phone and internet addiction, which are characterized by excessive use to the point of preoccupation and neglect of obligations [7]. Smart phone addiction refers to excessive use of
smart phones, similar to addiction to consumed chemicals, and can result in psychologically negative consequences [3]. Internet addiction is defined as loss of importance of
time not spent on the internet and is associated with excessive irritability and aggression
when deprived, and deterioration of an individual’s work, family and social life [8,9].
More broadly, smart phone and internet addiction can cause certain physical, mental, and
social health problems. It has been noted in the literature that in individuals who use smart
phones and the internet excessively, physical health problems can occur such as headaches
and deterioration of eyesight, sleep disorders, obesity, carpal tunneling, backaches, posture
and skeletal structure disorders, and physical fatigue [10,11]. Psychological health problems
can also result such as anxiety, depression, loneliness, hopelessness, insecurity, alexithymia,
and mental fatigue. In the social and occupational realms, social isolation, familial problems,
academic failure, low work performance, and inefficient time management may be observed
[5,12–14]. Additionally, negative lifestyle behaviors such as decreases in physical activity,
increased sedentary living, and eating disorders have been reported [2].
The groups under the highest risk for smart phone and internet addiction in Turkey are
children and young people from the new generation who have grown up with technology
and are more comfortable with it [15]. In a project conducted with the participation of
3,694 students called the ‘Photograph of Turkey’s Youth Regarding Technology Use and
Addiction’, it has been stressed that technology addiction should be evaluated as a
serious risk for young people [8]. For students, smart phones and the internet constitute
important technological developments that makes accessing information easier, across
a wide range of activities ranging from taking instant pictures to keeping course notes
[3]. This may lead to an increase in usage that can become excessive and may be associated with disruptions in sleep patterns, waking up late, and skipping breakfast, perhaps the
most important meal [7]. In another study [6], internet addiction was found to be related to
sleep problems, physical fatigue, headaches, and eyestrain. These negative consequences
may also be associated with fatigue and academic failure among students [10,16]. Finally,
in a study by Kahyaoglu et al. [17], students with higher internet addiction rates stated that
educational achievement became more difficult because of this addiction.
In smart phone and internet addiction studies of students conducted in Turkey, the
published data on these addictions in relation to academic success and fatigue are
limited (6,17–19). Given the large population of young people in Turkey, the vastly
increased internet and smart phone use in recent years suggests that more study on
their potentially ill effects are needed. The determination of whether internet and smartphone use leads to addiction, especially among younger people may be an important first
step in identifying one possible contributor to educational problems such as academic
failure and absenteeism as well as related physical problems such as fatigue.
FATIGUE: BIOMEDICINE, HEALTH & BEHAVIOR
43
Methods
Aim and design
This descriptive correlational study was performed in order to determine the relation
between communications technology addiction and academic success and fatigue.
Answers to the following questions were sought:
.
.
.
How common is smart phone and internet addiction in students at Sakarya University?
Is there a relationship between smart phone and internet addiction and academic success?
Is there a relationship between smart phone and internet addiction and fatigue?
Participants
The study population consisted of all students studying in undergraduate programs at the
Sakarya University, Turkey between February and June 2017. 743 students selected
through nonprobability sampling agreed to participate and completed our data collection
forms (response rate of 58.3%).
Data collection tools
Data was collected using a Student Identification Form, The Problematic Mobile Phone Use
Scale, the Internet Addiction Scale, and the Piper Fatigue Scale.
The Student Identification Form: In this 16-item form, prepared by the researchers in
accordance with the literature (2,3,5,10,12,19), personal information (age, gender,
smoking habit, etc.) was requested as well as respondents’ thoughts and behaviors regarding their smart phone and internet use habits (daily smart phone/internet use, reach the
internet etc.). Additionally, self-report grade point averages (4 point system) were used for
the evaluation of the students’ academic success.
The Problematic Mobile Phone Use Scale: This scale was developed by Bianchi and Phillips [18] and tested for validity and reliability in Turkish by Sar and Isiklar [1]. The scale consists of 27 items and takes approximately 25 min to fill out. Participants select their level of
agreement with each scale item from a range of answers between ‘does not define me at
all (1)’ and ‘defines me perfectly (5)’. Scores can range from 27–135 with higher scores indicating more problematic mobile phone use [1]. In this study, the Cronbach alpha value of
the scale was found to be 0.90.
Internet Addiction Scale: This 20-item scale was developed by Young [19] and tested for
validity and reliability in Turkish by Bayraktar [16]. Each item presents answer choices on a
six point Likert-type scale. The participants are expected to mark one answer for each item
from these choices: ‘never’, ‘rarely’, ‘sometimes’, ‘often’, ‘very often’, and ‘continuously’. The
range of possible scores is 0–100 with participants who score between ‘80’ and ‘100’ categorized as internet addicts, while scores between ‘50’ and ‘79’ are defined as showing
limited symptoms, and scores between ‘0’ and ‘49’ are considered as showing no symptoms [16]. In this study, the Cronbach alpha value of the scale was found to be 0.92.
The Piper Fatigue Scale: This scale was developed by Piper et al. [20] and tested for validity and reliability in Turkish by Can [21]. The scale consists of 22 items and measures the
44
F. TASKIN YILMAZ
subjective perception of an individual’s fatigue as evaluated through four sub-dimensions.
The behavior/intensity sub-dimension evaluates the effect of fatigue on daily life activities
and its intensity, the affective sub-dimension encompasses the emotional meaning given
to fatigue, the sensory sub-dimension reflects the mental, physical, and emotional symptoms of fatigue, and the cognitive/mental sub-dimension assesses fatigue effects on cognitive functions and mental status. The score for each sub-dimension is obtained by
summing the answers in that sub-dimension and dividing by the number of items. The
total fatigue score is obtained by summing up the answer choices of all items and dividing
it by the total number of items. Higher scores indicate higher perceived fatigue levels [21].
In this study, the Cronbach alpha value of the scale was found to be 0.96.
The data collection forms were administered to students within one course hour. The
forms were filled out by the students themselves and handed to the researchers. The completion of the data forms took approximately 20–25 min.
Evaluation of data
The statistical analysis was performed using the SPSS 22.0 package program. In statistical
evaluation, frequency distributions, mean values, Pearson correlation analysis, the Kruskal
Wallis test, and stepwise linear regression analysis were used.
Ethical approval
Before data collection, written permissions were obtained from the Cumhuriyet University
Non-Invasive Clinical Studies Board of Ethics (Decision number: 2015-12/13) and the institution where the study was conducted. Moreover, each participant joining the study was
informed about the content and voluntary participation and their verbal and written
consent was obtained.
Results
The mean age of the students was 20.93 ± 2.29 and 60% were female. Student-reported
areas of study were as follows: Faculty of Education (22.3%), Engineering Faculty (17%),
Faculty of Theology (21.2%), and the Faculty of Business Administration (15.4%). 28.8%
of the participants were sophomores, 72.1% resided at dormitories, and 17% used
tobacco. 7.3% of the students reported having a chronic disease, and 61.4% evaluated
their own health to be good.
The mean grade point average (4-point system) of the student participants was 2.59 ±
2.12, and 50.3% stated that their academic success was on a moderate level. Almost half of
the students reported daily smart phone use (45.1%) and daily internet use (43.3%) that
ranged from 4 to 7 h. 83.6% of students stated that they reached the internet via their
smart phones; 63.9% mostly used their smart phones to connect to the internet; and
58.3% used the internet to follow social media (Table 1).
The Problematic Mobile Phone Use Scale mean score (54.48 ± 15.77) and the Internet
Addiction Scale mean score (27.60 ± 16.19) were both found to be below average. i.e.
less problematic. 9.8% of the students exhibited internet addiction risk, while apparent
internet addiction was detected in 0.7%. The Piper Fatigue Scale mean score was also
FATIGUE: BIOMEDICINE, HEALTH & BEHAVIOR
45
Table 1. The academic success statuses of the students and their smart phone and internet use
behavior (N = 743).
n
Variables
Academic success average (in the 4 point system) (Mean ± SD)
Academic success evaluation
Very good
Good
Moderate
Bad
Very bad
Daily smart phone use
0–3 h
4–7 h
8–11 h
12 h and over
Daily internet use
0–3 h
4–7 h
8–11 h
12 h and over
Reach the internet
Mobile phone
Computer
Why use the most mobile phone?
Short family interviews
Chat
Internet
Other
Why use the most ınternet?
Follow social media
Chat
Email / electronic message
Online game
Course work-homework preparation
Online news, magazines etc. reading
Video, music, watching/ downloading
%
2.59 ± 2.12
36
189
374
122
22
4.8
25.4
50.3
16.4
3.0
247
335
127
34
33.2
45.1
17.1
4.6
257
322
125
39
34.6
43.3
16.8
5.2
623
120
83.6
16.2
46
201
475
21
6.2
27.1
63.9
2.8
433
119
12
35
12
30
102
58.3
16.0
1.6
4.7
1.6
4.0
13.8
found to be below average (3.78 ± 2.02), with students mostly experiencing sensory
fatigue (Table 2).
While no significant relationship was found between smart phone and internet addiction and academic success (p > .05), there was a significant positive association between
Table 2. The Distribution of the Problematic Mobile Phone Use Scale, Internet Addiction Scale, and
Piper Fatigue Scale mean scores of the students.
Scales
Problematic Mobile Phone Use
Scale
Internet Addiction Scale
No symptoms
Limited symptoms
Internet addicts
Piper Fatigue Scale
Behavior/intensity
Affective
Sensory
Cognitive/mental
Total
Range of obtainable scores
(min–max)
Range of scores
obtained
(min–max)
Mean ± SD
27–135
27–112
54.48 ± 15.77
0–100
0–49
50–79
80–100
0–100
0–64
49–75
83–100
27.60 ± 16.19
23.7611.92
57.85 ± 7.41
88.60 ± 6.73
0–10
0–10
0–10
0–10
0–10
0–10
0–10
0–10
0–10
0–10
3.47 ± 2.05
3.89 ± 2.51
3.98 ± 2.44
3.77 ± 2.36
3.78 ± 2.02
n
%
665
73
5
89.5
9.8
0.7
46
F. TASKIN YILMAZ
Table 3. The relationship between the Problematic Mobile Phone Use and Internet Addiction Scale
mean scores of the students and their academic success and Piper Fatigue Scale mean scores.
Variables
Problematic Mobile Phone Use Scale
Internet Addiction Scale
Academic success average
Piper Fatigue Scale
Behavior/intensity
Affective
Sensory
Cognitive/mental
Total
r = 0.047, p = .197
r = 0.013, p = .715
r = 0.275, p < .001
r = 0.187, p < .001
r = 0.180, p < .001
r = 0.197, p < .001
r = 0.240, p < .001
r = 0.344, p < .001
r = 0.210, p < .001
r = 0.169, p < .001
r = 0.198, p < .001
r = 0.261, p < .001
Table 4. The relationship between the academic success averages of the students and their Piper
Fatigue Scale mean scores according to their internet addiction levels.
Internet addiction levels
Variables
Academic success average
Piper Fatigue Scale
Behavior/intensity
Affective
Sensory
Cognitive/mental
Total
No symptoms
n = 665
Limited symptoms
n = 73
Internet addicts
n=5
Test/p
2.63 ± 2.41
2.23 ± 0.65
1.97 ± 0.59
7.929/p < .05
3.31 ± 2.00
3.78 ± 2.50
3.91 ± 2.43
3.66 ± 2.32
3.67 ± 1.99
4.69 ± 1.94
4.75 ± 2.44
4.60 ± 2.38
4.65 ± 2.51
4.67 ± 1.96
7.03 ± 1.63
6.40 ± 2.14
4.88 ± 2.94
4.73 ± 2.62
5.76 ± 1.90
41.652/p < .001
16.234/p < .001
6.407/p < .05
11.402/p < .001
22.972/p < .001
smart phone and internet addiction and all of the dimensions of fatigue, including
behavioral, affective, sensory and cognitive (p < .001). Compared to students showing
no addiction symptoms, students who were categorized as internet addicts were
found to have lower grade point averages (p < .05) and higher fatigue levels (p
< .001) (Table 4).
In Table 5, the relation between smart phone and internet addiction and academic
success was examined through multiple regression analysis. The variable of smart
phone and internet addiction was found not to be a significant predictor of academic
success (Table 5). The results of the multiple regression analysis regarding the Piper
Fatigue Scale scores (dependent variable) in relation to the predictor variables of Problematic Mobile Phone Use Scale and Internet Addiction Scale scores are given in Table 6.
Utilizing t tests to assess the significance of the regression coefficients, in compliance
with correlation analysis, the variable of smart phone addiction was seen to be a significant
predictor of fatigue scores (R = 0.24, R 2 = 0.058, F = 45.323, p = .000). Smart phone addiction scores alone explained 5.8% of the total variance in fatigue levels. Similarly, the variable of internet addiction was found to be a significant predictor of fatigue scores (R =
0.26, R 2 = 0.068, F = 54.112, p = .000). The internet addiction scores of the students alone
explained 6.8% of the total variance in fatigue levels.
Table 5. The results of the stepwise regression analysis regarding smart phone and internet addiction
levels as predictors of academic success.
Predictors
Smart phone addiction
Internet addiction
B
SE
β
t
p
0.015
0.004
0.012
0.011
0.047
0.013
1.292
0.365
.197
.715
FATIGUE: BIOMEDICINE, HEALTH & BEHAVIOR
47
Table 6. The results of the stepwise regression analysis regarding smart phone and internet addiction
as predictors of fatigue levels.
Predictors
Smart phone addiction
Internet addiction
B
SE
β
0.031
0.005
0.240
R = 0.24, R 2 = 0.058, F = 45.323, p < .001
0.033
0.004
0.261
R = 0.26, R 2 = 0.068, F = 54.112, p < .001
t
p
6.732
.000
7.356
.000
Discussion
Although university students are under greater risk of smart phone and internet addiction
[22], our findings revealed that smart phone and internet addiction levels of students at
Sakarya University, Turkey were below average (internet addiction in 0.7% and risk of internet addiction at 10%). In addition, no significant relationship was found between smart
phone and internet addiction and academic success; however, there was a significant
association between smart phone and internet addiction and fatigue symptoms. Also,
compared to students showing no addiction symptoms, students who were categorized
as internet addicts were found to have significantly lower grade point averages and
higher fatigue levels.
Student internet addiction across nations
In studies conducted abroad, the prevalence of internet addiction among university students was found to be 0.8% in Italy [23], 2.8% in Iran [24], 5.6% in China [25], 8.2% in
Europe and the USA [26], 11.6% in Greece [27], 17.9% in Taiwan [28], 16.2% in Poland
[29], and 18.3% in Great Britain [30]. In previous studies conducted in Turkey, the rate of
internet addiction among university students has been reported to be 1.3% [31] and 2%
[32], while studies on medical school students showed higher internet addiction rates of
6% [15], 7.2% [12], and 23.2% [33]. In our study, the low level of internet and smart phone
addiction may be due to the fact that students participating in the study were in departments requiring full-time education where most student time was spent in the school
environment which may be less subject to distraction. Thus, there may a selection bias
for students who enroll in these departments to be less susceptible to internet addiction.
Thus, to engage more representative samples, it may be beneficial to recruit students not
only in their particular faculties but also in various non-academic settings that students
frequent.
Technology addiction and academic success
Academic success in this study was defined by grade point averages. The academic
success or failure of a student is very important on a personal, family, and societal level
in Turkey. An academically successful and qualified workforce is accepted as the most
basic force for the development of a society [34]. Although no significant association
between the smart phone and internet addiction levels of students and their academic
success was found, the grade point averages of students in this study who scored
within the category of smart phone and internet addicts were significantly lower
48
F. TASKIN YILMAZ
compared to the students who exhibited no symptoms. A similar finding was obtained in a
study conducted by Bener et al. [6] on 2,350 students. In a number of other studies, smart
phone and internet addiction was found to have negative effects on academic success as
well [27, 29,31,35–41]. Students who have excessive smart phone and internet use may
have a harder time paying attention to course hours, participating in the active learning
process in class, and participating in the school activities expected of them.
Technology addiction and fatigue
In this study, smart phone and internet addiction was found to be associated with negative
effects on all dimensions of fatigue (behavioral, affective, sensory, and cognitive), with
higher fatigue levels as compared to students who exhibited no addiction symptoms. In
a study by Dol [10] of university students, a relationship was also found between daily
internet use and fatigue. Students in this study reported moderate fatigue as a result of
internet use, and fatigue was most noted in the areas of eyes, shoulders, and neck. In a
study conducted on Moroccan students, the physical and mental fatigue levels of students
who had internet addiction were found to be higher than those who did not [22]. In
another study conducted on medical students in Saudi Arabia, a quarter of students
were found to experience fatigue related to smart phone use [42]. When related behavioral factors such as lack of physical activity and lack of communication with the social
environment are added, fatigue and related negative effects can become more
pronounced.
Limitations
Since this cross-sectional study was conducted on a convenience sample of undergraduate students who studied at one university at a single time point, the results may be suggestive but cannot be generalized. Additionally, the information gained on smart phone
and internet addiction, fatigue, and grade point average were all based on self-report.
Conclusions
The smart phone and internet addiction rates of students at Sakarya University in Turkey
were found to be relatively low and technology addiction was found not to be associated
with academic success and fatigue levels. However, the academic success of students with
smart phone and internet addiction was lower and their fatigue levels higher as compared
to students with no few or addiction symptoms. Educational efforts via audiovisual media
may help to raise awareness on the negative relationship between smart phone and internet addiction and academic success as well as physical and psychological health. Additionally, it is suggested that studies be conducted with more representative samples that have
the power to examine the effect of smart phone and internet addiction on academic
success and fatigue over multiple time points in the university environment.
Disclosure statement
No potential conflict of interest was reported by the author.
FATIGUE: BIOMEDICINE, HEALTH & BEHAVIOR
49
Notes on contributors
Havva Sert has completed her Ph.D. from Marmara University Faculty of Health Sciences. She is an
assistant professor in the Faculty of Health Sciences in Sakarya University. She has published many
papers in reputed journals. Herpublications in nursing cover various aspects of nursing including
obesity, diabetes mellitus, diabetic foot ulcer, hypertension, tuberculosis, attitude, and healty
lifestyle.
Feride Taskin Yilmaz has completed her Ph.D. from Marmara University Faculty of Health Sciences.
She is an assistant professor in Highschool of Suşehri Health in Sivas Cumhuriyet University. She has
published many papers in reputed journals. Her publications in nursing cover various aspects of
nursing including diabetes mellitus, asthma, and aging. She has also studied in the fields of
student education and nursing education.
Azime Karakoç Kumsar has completed her Ph.D. from Marmara University Faculty of Health Sciences.
She is an assistant professor at Biruni University in the Nursing Department. Her priority academic
study areas are metabolic syndrome, type 2 diabetes, thyroid diseases and healthy lifestyle behaviors.
Dilek Aygin has completed her Ph.D. from Marmara University Faculty of Health Sciences. She is an
associate professor in the Faculty of Health Sciences in Sakarya University. She has published many
papers in reputed journals. Herpublications in nursing cover various aspects of nursing including
perioperative surgery, intensive care, sexuality, breast cancer, oncology, operation room nursing,
and ventilator-associated pneumonia.
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