Smart phone usage,sleep quality and depression in university students

Abstract

Background:

The smart phone contains various mobile applications specifically targeting their contents, such as information, messages, e-mail, education and entertainment towards youths. Problematic and excessive smart phone usage can cause many health problems including anxiety, depression and sleep disorders.

Aims:

The aim of this study is to analyse the relationship between smart phone usage, sleep quality and depression.

Methods:

Eight hundred and four students who owned smart phones were given the Information Form, Smart Phone Addiction Scale-Short Version, Pittsburgh Sleep Quality Index (PSQI) and Beck Depression Inventory (BDI). The descriptive statistics, independent sample t-test, one-way ANOVA, correlation analysis and multivariate regression analysis were used for analysis data.

Results:

The mean age of the students in the sample was 20.93 ± 2.44. It comprised female (65.0%) and male (35.0%) students. All of the students used smart phones. The daily smart phone usage duration was 7.85 ± 4.55 hour. According to the multivariate linear regression analysis results, significant relationships were statistically determined in the positive way between the smart phone addiction and PSQI point (p < .01) and BDI point (p < .01).

Conclusion:

Consequently, a relationship exists between smart phone usage, poor sleep quality and depressive symptoms in university students. The university students, whose depression point is high and sleep quality is poor, should be followed up with regarding the smart phone addiction.

Keywords

Smart phone addiction sleep depression university students

Background

At present, smart phones are ubiquitous and used in many cases, from information access to shopping. Usage of smart phones that have easy-access features and applications increases daily. The excessive and problematic usage due to the facilities that smart phone usage provides has brought smart phone addiction to the forefront (Demirci et al., 2015).

In the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), behavioural and substance addiction were separated (De-Sola Gutiérrez et al., 2016). Smart phone addiction is characterised by clinical features of excessive problematic usage and behavioural addiction that affect the daily life of the user (inoccupation, compulsive behaviour, control deficiency, functional deterioration, deprivation and tolerance). However, there are not specific diagnosis criteria determined and accepted for smart phone addiction (De-Sola Gutiérrez et al., 2016; Elhai et al., 2017).

According to Deloitte’s Mobile Consumer Survey (2017) results from 18 to 50 years of age who performed, while smart phone usage was 67.0% in 2015, this ratio was specified as 92.0% in 2017 in Turkey. Smart phone usage was 71.0% in America, 83.0% in South Korea and 54.0% in Japan (Alshobaili & AlYousefi, 2019). The Global Mobile User Survey study (2015) was carried out by 30 countries and 49,000 participants. Turkey, one of the countries in which the smart phone addiction is highest worldwide, has come into prominence due to its young population. The smartphone addiction increased incrementally with smart phone possession among young population (Deloitte Global Mobile User Survey, 2015).

The smart phone addiction rate was between 34.6% and 48.7% in the studies conducted among local university students in Turkey (Dikec & Kebapci, 2018; Ozcan, 2019). The smart phone addiction rate among university students in international literature is 97.8% in Iran (Mosalanejad et al., 2019) and 44.0% in South India (Kumar et al., 2019). The results showed a wide range. In these studies, different smart phone addiction scales and usage of inventory based on self-reporting could be reason for the variance (Kwon et al., 2013). Moreover, usage of smart phone applications based on the addiction scale could be another explanation (Csibi et al., 2018).

The excessive and problematic usage of smart phones causes physical problems, for example, sleep problems (falling asleep late, awakening from sleep, etc.; Demirci et al., 2015), eye health (Choi et al., 2018), musculoskeletal system (Kim & Kim, 2015), traffic and other severe accidents (Nasr Esfahani et al., 2019).

Also, excessive smart phone usage can cause certain mental, behavioural and social problems. Smart phone addiction is negatively correlated with various conceptions of well-being (e.g. Horwood, & Anglim, 2019; Tangmunkongvorakul et al., 2019). Smart phone addiction creates maladaptive behaviour problems and attention deficit, prevents school and work, decreases academic achivement and reduces real-life social interactions (Kuss & Griffiths, 2011). However, many people are not aware that smart phone addiction is serious and can negatively affect the thoughts, behaviours, tendencies, emotions and well-being of individuals (Alhassan et al., 2018). The depression and sleep problems were prevalent among the issues that affect the psychological health related to the smart phone addiction among the university students (Boumosleh & Jaalouk, 2017; Ezoe et al., 2019; Onal & Hisar, 2018). It was reported that depression in university students is more prevalent than in the general population. While the depression prevalence is between 10.0% and 85.0% in the international literature (Ibrahim et al., 2013; January et al., 2018), in our country, the risky student rate in terms of depression in the university students changed between 15.1% and 38.3% (Onal & Hisar, 2018; Ulas et al., 2015). A study of six university students in America found that 62.0% met the criteria for poor sleep and approximately 27.0% described their sleep quality as ‘quite poor’ or ‘very poor’ (Becker et al., 2018). Poor sleep quality in university students in Turkey was found to be between 41.1% and 59.0% (Aysan et al., 2014; Ozcan, 2019; Sari et al., 2015). Effects of sleep disorders on health have been analysed by many studies. There is a relationship between sleep deprivation and endothelial disfunction, a settled cardiovascular disease risk (Kohansieh & Makaryus, 2015). Moreover, sleep deprivation is also related to the increment of multi-metabolic disorder risk such as obesity, diabetes and insulin insensitivity (Kim & Kim, 2015).

The relationship between the level of the smart phone usage and sleep quality and depression should be studied in order to take preventive mental health measures. For this reason, the study aims to (1) study smart phone usage among university students and (2) determine the relationship between smart phone addictions, sleep quality and depression in university students.

Methods

Design and participants: This research was conducted using a cross-sectional design. The universe of research consisted of 1250 students studying at Iğdır University Vocational School of Health Services between 2017 and 2018. Iğdır University Vocational School of Health Services is a 2-year college which opened in 2009 and consists of five departments. The inclusion criteria of the research are using a smartphone, agreeing to participate in the study and signing informed consent. The exclusion criteria of the research are to complete the questionnaires incompletely and to want to leave the study. Forty-eight students were excluded because their scales were incompleted. Thus, in total, 804 students were included in the study. Approximately half (43.5%) of the students participating in the research constitutes students who study in the department of elderly and patient care at home. Before initiating the study, ethics committee approval (21) was obtained from the chairman of the ethics committee of Kafkas University on 1 February 2017. Then, permission was obtained from Iğdır University Vocational School of Health Services. Before collecting the research data, informed consent regarding the purpose of the research, its duration and the right to withdraw from the research was obtained in written form, and confidentiality was guaranteed. The research data were gathered by applying questionnaires in the classroom environment and data collection took one lesson hour. The researchers first informed the participants about the research and then distributed the questionnaires to those that volunteered.

Data collection tools

Information Form: The Information Form was prepared by the researchers and consisted of seven questions. In the first three questions, age, gender and department of study were asked of the participants. Then, smart phone usage characteristics were defined by open-ended questions (Do you have smart phone? What age did you start? What is your daily smart phone duration of use? What are the applications that you frequently use on the smart phone)?

Smartphone Addiction Scale-Short Version: The Smartphone Addiction Scale-Short Version (SAS-SV) was developed by Kwon et al. (2013) to measure the risk of smart phone addiction in teenagers. The scale comprises a total of 10 questions, with a six-point Likert type scaling. The items of the scale were scored from 1 to 6, and the scores varied between 10 and 60. It was stated that as the score from the scale increases, the risk for addiction also increases. The scale was one factor and did not possess sub-scales. Its Turkish adaptation was carried out by Noyan et al. (2015). Chronbach’s alpha coefficient of the scale was .867 and reliability coefficient of test/retest was .926. No cut-off point was determined for the scale. The evaluation was made over the total points (Noyan et al., 2015).

Beck Depression Inventory: This self-report scale developed by Beck (1961) evaluates the level and severity of the depressive symptoms of the participants in terms of the depression and consists of 21 questions. The score for each problem varied between 0 and 3, and the total score varied between 0 and 63. Its Turkish adaptation was carried out by Hisli (1989). The Cronbach’s alpha coefficient was .800 in the reliability analysis. In split-half reliability, r = 0.740. In the conpresent validity method, the Beck Depression Inventory (BDI) and The Minnesota Multiphasic Personality Inventory depression sub-scale were applied and their correlation was r = 0.500. The cut-off point has been determined as 17 for the Turkish version. For severity, they were interpreted as 0–9 = Minimal, 10–16 = Mild, 17–9 = Moderate and 30–63 = Severe (Hisli, 1989).

Pittsburgh Sleep Quality Index: The Pittsburgh Sleep Quality Index (PSQI) was used to evaluate sleep quality. The PSQI developed by Buysse et al. (1989) it eas used it Turkish validity and reliability studies by Agargun et al. (1996). With the help of PSQI, a reliable, valid and standard measurement of sleep quality was provided. A reliable separation on an intended level was conducted between the ‘People who can sleep well’ and ‘People who cannot sleep well’. The PSQI, which evaluates the sleep quality of the last month, contained 24 questions. The PSQI consisted of one total score and seven sub-dimensions. These components were self-sleep quality; sleep latency; sleep duration; customary sleep activity; sleep disorder; sleeping pill usage; and daytime sleep dysfunction. The total PSQI score varies between 0 and 21. As the total scale score increases, sleep quality is impaired. The sleep qualities of those who have a total score of 5 and below are evaluated as ‘good’, and those who have higher than 5 are evaluated as ‘bad’ (Agargun et al. 1996).

Data analysis: Statistical analysis of the data was carried out in the Statistical Package for the Social Sciences (SPSS) 20.0. The definitive statistics were used to determine the sociodemographic data, smart phone usage features, sleep depression point averages and data distribution (frequency, mean, standard deviation minimum and maximum values, kurtosis and skewness values). The independent sample t-test, one-way ANOVA and Pearson correlation analysis were used to test whether a difference occurred between the SAS-SV total points to evaluate relationship existence in the data showing a normal distribution. Finally, the multivariate linear regression analysis test was applied to determine effect level of all variables (sociodemographic features, smart phone usage features, PSQI total point and BDI total point) on the SAS-SV. Only statistically significant variables were included in the multivariable regression model. The results were assessed with a 95.0% confidence interval at p < .05 significance level.

Results

Sociodemographic characteristics of the participants

All students use smart phones. The mean age of the students was 20.93 ± 2.44, most of them (n = 521, 65.0%) were female and continued their education at the department of patient care at home (n = 169, 21.0%). The age of introduction to the smart phone was 17.22 ± 2.38. The daily smart phone usage duration was 7.85 ± 4.55. The most frequent smart phone activities were social media use (n = 594, 94.1%). (Instagram, Facebook, Twitter, etc.; Table 1).

Table 1.

Sociodemographic characteristics of the participants.

Variables	n	%
Age
17–19	178	22.1
20–25	594	73.9
26 and over	31	3.9
Gender
Female	521	65.0
Male	283	35.0
Students’ course of study
Elderly care	181	22.5
Patient care at home	169	21.0
Medical documentation and secretariat	124	15.4
Medical laboratory techniques	103	12.8
First and immediate aid	91	11.3
Child development	86	10.7
Environment health	50	6.2
***Smart phone activities
Using social media (Instagram, Facebook, Twitter, etc.)
Yes	594	74.1
No	208	25.9
Communication-meeting (WhatsApp, Message, Messenger, etc.)
Yes	550	68.6
No	252	31.4
Listening to music/watching film/video (YouTube, etc.)
Yes	239	29.8
No	563	70.2
Research/doing homework, reading newspaper and book (Google, Safari, etc.)
Yes	285	35.5
No	517	60.5
Playing a game
Yes	154	19.2
No	648	80.8
Taking a photo
Yes	24	3.0
No	776	97.0
	Mean	SD
Starting age of smart phone use	17.22	2.38
Daily duration of smart phone use	7.85	4.55

***

More than one option selected.

Sociodemographic variables and SAS-SV total point means comparison

It was determined that there was not a significant relationship or difference between the students’ age, gender, the course that the students study, the starting age for the smart phone and some smart phone activities (communication-meeting, listening to music/watching film/video and taking a photo) and the SAS-SV point (p > .05). On the other hand, there was a positive and advanced-level significant relationship between daily smart phone usage duration and the SAS-SV point (r = 0.250, p = .000). The SAS-SV point average was statistically and significantly higher than those who use a smart phone for the social media (t = 4.330, p = .000), those who use it for the game (t = 2.759, p = .006) and those who do not use it for homework (t = −2.113, p = .035; Table 2).

Table 2.

Sociodemographic variables and SAS-SV total point means comparison.

Variables	SAS-SV
	Mean ± SD	r
Starting age of smart phone use	17.22 ± 2.38	−0.063*
Daily duration of smart phone use	7.85 ± 4.55	0.250**
	Mean/SD	t/F
Age
17–19	31.69 ± 11.82
20–25	32.34 ± 11.32	1.349
26 and over	29.03 ± 11.88
Gender
Female	32.56 ± 11.52	1.651
Male	31.17 ± 11.29
Students’ course of study
Elderly care	31.80 ± 11.33
Patient care at home	32.97 ± 11.33
Medical documentation and secretariat	30.04 ± 10.78	1.709
Medical laboratory techniques	34.36 ± 11.80
First and immediate aid	31.73 ± 12.08
Child development	32.32 ± 11.39
Environment health	30.54 ± 11.66
***Smart phone activities
Using social media (Instagram, Facebook, Twitter, etc.)
Yes	33.09 ± 11.33	4.330**
No	29.14 ± 11.26
Communication-meeting (WhatsApp, Message, Messenger, etc.)
Yes	32.37 ± 11.47	1.094
No	31.42 ± 11.40
Listening to music/watching film/video (YouTube, etc.)
Yes	31.61 ± 11.63	−0.746
No	32.27 ± 11.36
Research/doing homework, reading newspaper and book (Google, Safari, etc.)
Yes	30.92 ± 11.26	−2.113*
No	32.70 ± 11.49
Playing a game
Yes	34.35 ± 11.59	2.759*
No	31.53 ± 11.34
Taking a photo
Yes	32.16 ± 11.54	0.033
No	32.08 ± 11.44

Note. SAS-SV = smartphone addiction scale-short version; SD = standard deviation; n = the percentages were taken over; t = t-test value; F = ANOVA value; r = correlation coefficient.

p<.05. **p<.01. ***More than one option selected.

Multivariate linear regression analyses of factors associated with smart phone addiction in university students (n = 804)

As shown in Table 3, the multivariate linear regression analysis was performed to define the variables that correlated with the SAS-SV score. The regression model was found statistically significant (F = 19.565; p < .01). The model explained 14.2% of the variance in smartphone addiction (R² = 0.150, adjusted R² = 0.142). The significant relationships were statistically determined in the positive way between the smart phone addiction and daily smart phone usage (B = 0.531; p < .01); social media usage (β: .124; p < .01); playing game (β: .083; p < .05); PSQI point (β: .099; p < .01); and BDI point (B = 0.194; p < .01). On the other hand, there is not a significant statistical relationship between smart phone usage starting age and research on the internet and doing homework, reading newspaper and book (p > .05).

Table 3.

Multivariate linear regression analyses of factors associated with smart phone addiction in university students (N = 804).

Variables	SAS-SV
	B	SE B	β	t	p value
Constant	20.002	3.210	–	6.231	.000
Starting age of smart phone use	−0.003	0.162	−.001	−0.017	.986
Daily smart phone usage duration	0.531	0.086	.211	6.195	.000**
Using social media (0, No; 1, Yes)	3.240	0.892	.124	3.632	.000**
Research/doing homework, reading newspaper and book (0, No; 1, Yes)	0.073	0.812	.003	0.090	.928
Playing game (0, No; 1, Yes)	2.389	0.960	.083	2.488	.013*
PSQI (0, Good Sleep Quality; 1, Poor Sleep Quality)	2.340	0.853	.099	2.743	.006*
BDI	0.194	0.038	.187	5.155	.000**
R ²					.150
Adjusted R²					.142

Note. SAS-SV = smart phone addiction scale-short version; PSQI = Pittsburgh sleep quality index; BDI = Beck depression inventory; B = unstandardised beta; SE B = standard error for the unstandardised beta; β = standardised beta; t = t-test statistic.

Dependent variable = SAS-SV: smart phone addiction scale-short version.

p < .05. **p < .01.

Discussion

In this study, we specified the relationship between smart phone usage, sleep quality and depression in university students. The results of the multivariate linear regression analysis, have showed that increased daily smart phone usage duration, the social media usage, the game playing, the poor sleep quality, the depressive symptom severity consequences were significantly positively correlated with the smart phone addiction.

In our study, the smart phone usage amount, social media usage, playing games and smart phone addiction are related in a positive way. Smart phones, of which features increase as time passes, have become the most practical and most preferred mobile devices. The fact that smart phones are used so often in our daily applications and are indispensable caused us to face the concept of ‘smartphone addiction’. The supreme reason for the increasing addiction risk is the increasing variety of social media services (Gundogmus et al., 2019). Gundogmus et al. (2019) determined that excessive of smartphone usage with university students’ social media networks is potentially addictive. Similarly, Yang (2016) determined a relationship between the time spent on smart phones and smart phone addiction in a sampling occurring from the university students. The overuse of social media should be viewed as a public health issue in order to address smartphone addiction leading to a more healthy use. The necessary measures should be taken to use properly, especially among the students attending the university.

In the present study, there is a significant positive relationship between the sleep quality disorder and smart phone addiction. Sleep quality is very important for university students in terms of physical and psychosocial health (Kim et al., 2019). There are many studies revealing the relationship of smart phone addiction and sleep quality disorders in university students (Chen et al., 2017; Gundogmus et al., 2019; Kim et al., 2019). It has been also compared to the studies causally analysing the smart phone addiction and sleep quality relationship. Gundogmus et al. (2019) determined that excessive smart phone usage together with social media network addiction affected sleep quality negatively, by which they evaluated social media usage, smart phone addiction and sleep quality of 139 university students. The smart phone addiction points have shown significant positive correlations with sleep disorders, day functional disorder, subjective and global sleep quality points in 319 Turkish university students (Demirci et al., 2015). In the study conducted with 435 Saudi university students, nine of 10 students used their smart phone before going to sleep. In the same study, smart phone usage of more than 60 minutes, especially before going to sleep, was correlated with poor sleep quality (Alshobaili & AlYousefi, 2019).

In the present study, there is a positive relationship between smart phone addiction and depressive symptom severity. This results obtained in the present study are parallel with the literature revealing the smart phone addiction and depression relationship (Boumosleh & Jaalouk, 2017; Ezoe et al., 2019; Kim et al., 2019; Yang, 2016). Depression is significantly related to both smart phone addiction (Augner & Hacker, 2011) and problematic mobile phone usage (Yen et al., 2009) in university students in Lebanon and Austria. A systematic research of 23 articles show that associated between the depression and the problematic smart phone usage (Elhai et al., 2017). Moreover, smart phone addiction can be a factor leading to depression implicitly or through the intervention effect. Being seen as unhealthy life style behaviours and sleep disorders more in the persons whose smart phone addiction is high can increase the inclination to depression (Alhassan et al., 2018). The other systematic research of 14 articles show that increased risks of poor sleep quality, depression, and anxiety in people with the association of problematic smartphone use (Yang et al., 2020).

Consequently, in the present study, smart phone addiction is an important public health problem related to bad sleep quality and depression. Moreover, while sleep disorders take place among depression symptoms, depression can develop related to the sleep disorders. Sleeping late and getting up early can increase somatic and depressive complaints such as concentration problems, peevishness, tiredness, unwillingness to participate in physical or social activities, headache, sleep deprivation and prevalent bodily ache (Yucel & Unsalver, 2019). Therefore, studies revealing more causality related to smart phone addiction should be conducted. Moreover, smart phone addiction prevention strategies should be more focused. Healthcare providers administering public health services should cooperate with educators providing preventive mental health services. University students can be educated about the relationship between smartphone usage, depression indicators and sleep disorders with this cooperation. Moreover, students can learn to develop healthy skills in smartphone usage.

Limitations: The present study has several limitations. Smart phone usage, sleep quality and depression indications were reported by the students. Specifically, the sleep studies have to be evaluated with more objective methods in order to obtain higher validity. Also, a higher-quality evaluation can be made through applications that record the smart phone usage period. The data reflect the subjective perceptions and statements of the participants. Therefore, the sleep period and nature should be measured with higher-quality smart phone usage evaluation instruments. In the present study, frequently used smart phone applications were determined; however, the duration of use has been evaluated generally. In future studies, during what time durations the smart phone usage occurs can be also evaluated. All participants were university students with a higher education level which doesn’t represent the total population. The present study used a cross-sectional design limiting the results, which is not ideal for evaluating causal relationships. Another limitation of this study was that two times more female students were sampled than males; the study found that female students suffered more from depression and poor sleep quality than males. This condition must be considered when the results are evaluated. A longitudinal study involving long term monitoring and different samples from participants of different ages, genders and education levels would better explain the cause and effect relationship and generalise the research findings. Besides those limitations, the results maintain their importance in increasing awareness of the subject.

Conclusion

These results can be used for the prevention of smart phone addiction. The smart phone usage habits and amounts were found significant in developing smart phone addiction in university students. Youths should be made aware of their habits that will cause excessive smart phone usage and prevent it. The training and intervention studies are required to be implemented to reduce unnecessary smart phone usage, regulate media literacy and develop the sleep hygiene of university students. There is a need of proper health trainings and interventions to prevent the addiction, deal with the addiction and increase mental well-being for the university students.

Footnotes

Acknowledgements

This study is accepted and presented in V st. International IX st. Natioanal Psychiatric Nurses Congress, 20–23 November 2018 Antalya/Turkey.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Fadime Kaya

References

Agargun

M.Y.

Kara

Anlar

(1996). The validity and reliability of the Pittsburgh sleep quality index. Turkish Journal of Psychiatry, 7(2), 107–111.

Alhassan

A. A.

Alqadhib

E. M.

Taha

N. W.

Alahmari

R. A.

Salam

Almutairi

A. F.

(2018). The relationship between addiction to smartphone usage and depression among adults: A cross sectional study. BMC Psychiatry, 18(1), 148. https://doi.org/10.1186/s12888-018-1745-4

Alshobaili

F. A.

AlYousefi

N. A.

(2019). The effect of smartphone usage at bedtime on sleep quality among Saudi non-medical staff at King Saud University Medical City. Journal of Family Medicine and Primary Care, 8(6), 1953–1957. https://doi.org/10.4103/jfmpc.jfmpc_269_19

Augner

Hacker

(2011). Associations between problematic mobile phone use and psychological parameters in young adults. International Journal of Public Health, 57(2), 437–441. https://doi.org/10.1007/s00038-011-0234-z

Aysan

Karaköse

Zaybak

İsmailoğlu

E. G.

(2014). Sleep quality among undregraduate students and influencing factors. Journal of Dokuz Eylül University School of Nursing, 7(3), 193–198.

Beck

A. T.

Ward

C. H.

Mendelson

(1961). An inventory for measuring depression. Archives of General Psychiatry, 4(6), 561–571. https://doi.org/10.1001/archpsyc.1961.01710120031004

Becker

S. P.

Jarrett

M. A.

Luebbe

A. M.

Garner

A. A.

Burns

G. L.

Kofler

M. J.

(2018). Sleep in a large, multi-university sample of college students: Sleep problem prevalence, sex differences, and mental health correlates, Sleep Health, 4(2), 174–181. https://doi.org/10.1016/j.sleh.2018.01.001

Boumosleh

J. M.

Jaalouk

(2017). Depression, anxiety, and smartphone addiction in university students-A cross sectional study. PLoS One, 12(8), e0182239. https://doi.org/10.1371/journal.pone.0182239

Buysse

D. J.

Reynolds

C. F.

III. Monk

T. H.

Berman

S. R.

Kupfer

D. J.

(1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2), 193–213. https://doi.org/10.1016/0165-1781(89)90047-4

10.

Chen

Liu

Ding

Ying

Wang

Wen

(2017). Gender differences in factors associated with smartphone addiction: A cross-sectional study among medical college students. BMC Psychiatry, 17(1), 341. https://doi.org/10.1186/s12888-017-1503-z

11.

Choi

J. H.

Kim

K. S.

Kim

H. J.

Joo

S. J.

Cha

H. G.

(2018). Factors influencing on dry eye symptoms of university students using smartphone. Indian Journal of Public Health Research & Development, 9(11), 964–969. https://doi.org/10.5958/0976-5506.2018.01583.8

12.

Csibi

Griffiths

M. D.

Cook

Demetrovics

Szabo

(2018). The psychometric properties of the smartphone application-based addiction scale (SABAS). International Journal of Mental Health and Addiction, 16(2), 393–403. https://doi.org/10.1007/s11469-017-9787-2

13.

Deloitte Global Mobile User Survey. (2015). Turkey executive summary. https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/technology-media-telecommunications/deloitte-global-mobil-kullanici-anketi-2015-f.pdf

14.

Deloitte Global Mobile User Survey. (2017). The place of mobile technologies in our digitalized life. Istanbul Turkey Executive Summary. https://www2.deloitte.com/content/dam/Deloitte/tr/Documents/technology-media-telecommunications/deloitte_gmcs_2017.pdf

15.

Demirci

Akgonul

Akpinar

(2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of Behavioral Addictions, 4(2), 85–92. https://doi.org/10.1556/2006.4.2015.010

16.

De-Sola Gutiérrez

Rodríguez de Fonseca

Rubio

. (2016). Cell-phone addiction: A review. Frontiers in Psychiatry, 7, 175. https://doi.org/10.3389/fpsyt.2016.00175

17.

Dikec

Kebapci

(2018). Smartphone addiction level among a group of university students. Journal of Dependence, 19(1), 1–9.

18.

Elhai

J. D.

Dvorak

R. D.

Levine

J. C.

Hall

B. J.

(2017). Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. Journal of Affective Disorders, 207, 251–259. https://doi.org/10.1016/j.jad.2016.08.030

19.

Ezoe

Iida

Inoue

Toda

(2019). Smartphone addiction and sleep quality associated with depression in university students in Japan. International Journal of Cyber Behavior, Psychology and Learning, 9(4), 22–31. https://doi.org/10.4018/IJCBPL.2019100102

20.

Gundogmus

İ.

Aydin

M. B.

Aydin

M. S

. (2019). The impact of using social network services on the smartphone addiction and sleep quality in university student. Bulletin of Clinical Psychopharmacology, 29, 44–45.

21.

Hisli

(1989). The validity and reliability of Beck depression inventory for university students. Turkish Journal of Psychology, 7(23), 3–13.

22.

Horwood

Anglim

(2019). Problematic smartphone usage and subjective and psychological well-being. Computers in Human Behavior, 97, 44–50. https://doi.org/10.1016/j.chb.2019.02.028

23.

Ibrahim

A. K.

Kelly

S. J.

Adams

C. E.

Glazebrook

(2013). A systematic review of studies of depression prevalence in university students. Journal of Psychiatric Research, 47(3), 391–400. https://doi.org/10.1016/j.jpsychires.2012.11.015

24.

January

Madhombiro

Chipamaunga

Ray

Chingono

Abas

(2018). Prevalence of depression and anxiety among undergraduate university students in low-and middle-income countries: A systematic review protocol. Systematic Reviews, 7(1), 57. https://doi.org/10.1186/s13643-018-0723-8

25.

Kim

H. J.

Kim

J. S.

(2015). The relationship between smartphone use and subjective musculoskeletal symptoms and university students. Journal of Physical Therapy Science, 27(3), 575–579. https://doi.org/10.1589/jpts.27.575

26.

Kim

H. J.

Min

J. Y.

Kim

H. J.

Min

K. B.

(2019). Association between psychological and self-assessed health status and smartphone overuse among Korean college students. Journal of Mental Health, 28(1), 11–16. https://doi.org/10.1080/09638237.2017.1370641

27.

Kim

S. H.

Min

J. W.

Park

B. K.

(2019). The effect of smartphone addiction and stress on sleep quality among university students. Journal of the Korea Academia-Industrial Cooperation Society, 20(4), 112–120. https://doi.org/10.5762/KAIS.2019.20.4.112

28.

Kohansieh

Makaryus

A. N.

(2015). Sleep deficiency and deprivation leading to cardiovascular disease. International Journal of Hypertension, 615681, 1–5. https://doi.org/10.1155/2015/615681

29.

Kumar

V. A.

Chandrasekaran

Brahadeeswari

(2019). Prevalence of smartphone addiction and its effects on sleep quality: A cross-sectional study among medical students. Industrial Psychiatry Journal, 28(1), 82–85. https://doi.org/10.4103/ipj.ipj_56_19

30.

Kuss

D. J.

Griffiths

M. D.

(2011). Online social networking and addiction—A review of the psychological literature. International Journal of Environmental Research and Public Health, 8(9), 3528–3552. https://doi.org/10.3390/ijerph8093528

31.

Kwon

Kim

D. J.

Cho

Yang

(2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS One, 8(12), e83558. https://doi.org/10.1371/journal.pone.0083558

32.

Mosalanejad

Nikbakht

Abdollahifrad

Kalani

(2019). The prevalence of smartphone addiction and its relationship with personality traits, loneliness and daily stress of students in Jahrom University of Medical Sciences in 2014: A cross-sectional analytical study. Journal of Research in Medical and Dental Science, 7(2), 131–136.

33.

Nasr Esfahani

Arvin

Song

Sze

N. N

. (2019). Prevalence of cell phone use while driving and its impact on driving performance, focusing on near-crash risk: A survey study in Tehran. Journal of Transportation Safety & Security. Advance online publication. https://doi.org/10.1080/19439962.2019.1701166

34.

Noyan

C. O.

Darçin

A. E.

Nurmedov

Yilmaz

Dilbaz

(2015). Validity and reliability of the Turkish version of the smartphone addiction scale-short version among university students. Anatolian Journal of Psychiatry, 16(S1), 73–82. https://doi.org/10.5455/apd.176101

35.

Onal

G. Ş.

Hisar

K. M.

(2018). The relationship between insomnia severity and depression symptoms in university students and the effect of depression treatment on insomnia severity. Gumushane University Journal of Health Sciences, 7(1), 125–130.

36.

Ozcan

(2019). Sleep quality in Pamukkale University students and its relationship with smartphone addiction (Unpubished MD thesis). Pamukkale University Deparment of Community Health, Denizli.

37.

Sari

O. Y.

Uner

Buyukakkus

Bostanci

E. O.

Celiksoz

A. H.

Budak

(2015). Sleep quality and some factors affecting sleep quality in the students living in the residence hall of a university. TAF Preventive Medicine Bulletin, 14(2), 93–100. https://doi.org/10.5455/pmb.1-1408013434

38.

Tangmunkongvorakul

Musumari

P.M.

Thongpibul

Srithanaviboonchai

Techasrivichien

Suguimoto

S.P.

Kihara

(2019). Association of excessive smartphone use with psychological well-being among university students in Chiang Mai, Thailand. PLoS One, 14(1), e0210294. https://doi.org/10.1371/journal.pone.0210294

39.

Ulas

Tatlibadem

Nazik

Sonmez

Uncu

(2015). Depression frequency and associated with university students agents. Celal Bayar University Health Sciences Institute Journal, 2(3), 71–75.

40.

Yang

K. M.

(2016). The effect of self-esteem and depression on smartphone addiction among university students. Journal of the Korea Convergence Society, 7(1), 113–123. https://doi.org/10.15207/JKCS.2016.7.1.113

41.

Yang

Liao

(2020). Association of problematic smartphone use with poor sleep quality, depression, and anxiety: A systematic review and meta-analysis. Psychiatry Research, 284, 112686. https://doi.org/10.1016/j.psychres.2019.112686

42.

Yen

C. F.

Tang

T. C.

Yen

J. Y.

Lin

H. C.

Huang

C. F.

Liu

S. C.

C. H.

(2009). Symptoms of problematic cellular phone use, functional impairment and its association with depression among adolescents in southern Taiwan. Journal of Adolescence, 32(4), 863–873. https://doi.org/10.1016/j.adolescence.2008.10.006

43.

Yucel

E. E.

Unsalver

B. O.

(2019). The relationship between smartphone using style and sleep quality and psychiatric symptoms among a foundation university students. The European Research Journal, 538377, 1–11. https://doi.org/10.18621/eurj.538377