Relationship of Smartphone Use Severity with Sleep Quality, Depression and Anxiety in University Students: A Critical Review

This critical review analyzes the study conducted by Demirci, Akgönül and Akpinar (2015) on the relationship between smartphone use severity and mental state in university students. The study found a positive correlation between smartphone addiction and depression, anxiety, and sleep quality. The review evaluates the study design, hypothesis, and limitations.

Running head: CRITICAL REVIEW
Relationship of smart-phone use severity with sleep quality, depression and anxiety in
university students: A critical review
Name of the Student
Name of the University
Author Note

1
CRITICAL REVIEW
Introduction
Woods and Scott (2016) stated that with an increase in the access of the Smartphone
there is increase in the use of social media. This tendency is mostly popular among the
teenagers or the students. Woods and Scott (2016) are of the opinion that high rate of surfing
of social media among the young adults or the teenagers is associated with the disruption in
the sleep quality along with adverse mental health consequences like increase sense of
depression anxiety and poor-self esteem. Jasso-Medrano and López-Rosales (2018) high use
social media cause addiction and this hampers the mental balance. The following paper aims
to analyze the study conducted by Demirci, Akgönül and Akpinar (2015). The assignment
will initiate with a brief summary of the study followed by critical evaluation of the study
design and hypothesis.
Body
Summary
Demirci, Akgönül and Akpinar (2015) conducted a study with an aim to investigate
the relationship between use of Smartphone and mental state. They mainly studied how the
use of Smartphone hampers the quality of sleep or increase the sense of depression and
anxiety among the university students. Demirci, Akgönül and Akpinar (2015) selected 319
university students of them 203 were females and 116 were males with the mean age group of
20.5 +/- 2.45. The selected group of participants were further subdivided into three groups
first group was composed of non-Smartphone user group, the second group was low smart-
phone user group and third group used smart-phone at high rate of severity index. The sleep
and the level of depression and anxiety of the all the students were measured via Pittsburgh
Sleep Quality Index, Depression Inventory and Beck Anxiety Inventory respectively. The
analysis of the results revealed that level of Smartphone addiction is higher among the

2
CRITICAL REVIEW
females in comparison to makes. The level of dysfunctional index at time along with
depression and anxiety were higher among the high Smartphone user group in comparison to
the low Smartphone user groups. Thus the author highlighted a positive correlation between
Smartphone Addiction Scale scores and the level of depression, anxiety and sleep quality.
Thus the author concluded that overuse of Smartphone, increases the sense of depression,
anxiety and this in turn hampers the quality of sleep (Demirci, Akgönül & Akpinar, 2015).
The results elucidated by Demirci, Akgönül and Akpinar (2015) are in accordance with the
results highlighted by Sapacz, Rockman and Clark (2016). Sapacz, Rockman and Clark
(2016) argued that the use of Smartphone cause behavioural addiction along with the
generation of social anxiety. This behavioural addiction hampers the quality of sleep and the
sense of social anxiety heightens the depression and anxiety level.
Critical evaluation
The main idea presented by Demirci, Akgönül and Akpinar (2015) is high severity of
Smart phone use increases the level of depression, anxiety and quality of sleep among the
university students. Similar hypothesis was previously highlighted in the study of Arora et al.
(2014). Arora et al. (2014) stated that use of Smartphone during midnight decrease the sleep
quality and leads to para-somnia. The cross-sectional study conducted by Arora et al. (2014)
stated that nighttime use of technologies present in the application of Smartphone like
listening to music is associated with greater cognitive difficulty in shutting off. This difficulty
in shutting-off hampers the sleep parameters and this in turn disrupts the quality of sleep.
Sapacz, Rockman and Clark (2016) also highlighted increase use of Smartphone disrupts the
sleep quality. Lee et al. (2014) argued that the use of Smartphone creates several
psychological imbalance and they mainly emphasized on the social interaction anxiety. Thus,
the aim of the study selected by the authors Demirci, Akgönül and Akpinar (2015), is unique
this is because, the previous studies mainly elucidated that relationship between sleep and the

3
CRITICAL REVIEW
use of Smartphone and not how Smartphone increases the sense of anxiety and depression.
Demirci, Akgönül and Akpinar (2015) selected university students as the main focus group,
this approach is again unique as Reese Bomhold (2013) stated that use of Smart phones are
higher among the students especially the university students.
They mainly selected 319 university students for the study. According to Marshall et
al. (2013), use of high sample size decreases the overall rate of biased results in the study
however, the sample size of this study is small and the same has been highlighted by the
authors of the study. In order to access the sleep quality of the selected group of students, the
authors used Pittsburgh Sleep Quality Index (PSQI). According to Mollayeva et al. (2016),
PSQI is an important screening tool for sleep dysfunction. The tool is reliable with moderate
to high structural validity over a variety of samples. In order to measure the sense of
depression and anxiety, Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI)
were used respectively. Upton (2013) argued that both BDI and BAI is an effective tool to
measure the mental health state with accuracy.
Of all participants, 78% were Smartphone users and 22% were not Smartphone user.
The average Smartphone Addiction Scale (SAS) of Smartphone users is 75.68. The rationale
behind this is justified by a Turkish version of the SAS study involving 301 university
students (Demirc et al., 2014). However, the main limitations of the study are, the authors
mainly selected university students and did not used SAS for the non-Smartphone user group
(as per the reference of Table: 1 in the study). Fraley and Hudson (2014) argued that
longitudinal studies involving samples from different age group and educational background
might produce different results in comparison to a particular focus group. Thus, the
implications of the study conducted by Demirci, Akgönül and Akpinar (2015) might not be
applicable to other focus groups. Moreover, the authors conducted cross-sectional study
design. Sedgwick (2014) stated that it is not best way to evaluate casual relations and thus

4
CRITICAL REVIEW
might limit the study results. Thus, it can be said that the authors were aware about the
limitations of the study.
Conclusion
Thus from the critical analysis of the above article, it can be concluded that the study
score high in the domain of unique hypothesis, sample size but limits in the domains of the
study design and the selection of the focus group. Moreover, the tools which are used to
measure sleep quality and the level of depression or anxiety were as per research standards.
The critical analysis also revealed that results of the cross-sectional study with particular
focus group might not be applicable other focus in order to replicate similar results.

5
CRITICAL REVIEW
References
Arora, T., Broglia, E., Thomas, G. N., & Taheri, S. (2014). Associations between specific
technologies and adolescent sleep quantity, sleep quality, and parasomnias. Sleep
medicine, 15(2), 240-247.
Demirci, K., Akgönül, M., & Akpinar, A. (2015). Relationship of smartphone use severity
with sleep quality, depression, and anxiety in university students. Journal of
behavioral addictions, 4(2), 85-92.
Demirci, K., Orhan, H., Demirdas, A., Akpinar, A., & Sert, H. (2014). Validity and reliability
of the Turkish Version of the Smartphone Addiction Scale in a younger
population. Klinik Psikofarmakoloji Bülteni-Bulletin of Clinical
Psychopharmacology, 24(3), 226-234.
Fraley, R. C., & Hudson, N. W. (2014). Review of intensive longitudinal methods: an
introduction to diary and experience sampling research.
Jasso-Medrano, J. L., & López-Rosales, F. (2018). Measuring the relationship between social
media use and addictive behavior and depression and suicide ideation among
university students. Computers in Human Behavior.
Lee, Y. K., Chang, C. T., Lin, Y., & Cheng, Z. H. (2014). The dark side of smartphone usage:
Psychological traits, compulsive behavior and technostress. Computers in human
behavior, 31, 373-383.
Marshall, B., Cardon, P., Poddar, A., & Fontenot, R. (2013). Does sample size matter in
qualitative research?: A review of qualitative interviews in IS research. Journal of
Computer Information Systems, 54(1), 11-22.

6
CRITICAL REVIEW
Mollayeva, T., Thurairajah, P., Burton, K., Mollayeva, S., Shapiro, C. M., & Colantonio, A.
(2016). The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in
clinical and non-clinical samples: a systematic review and meta-analysis. Sleep
medicine reviews, 25, 52-73.
Reese Bomhold, C. (2013). Educational use of smart phone technology: A survey of mobile
phone application use by undergraduate university students. Program, 47(4), 424-436.
Sapacz, M., Rockman, G., & Clark, J. (2016). Are we addicted to our cell
phones?. Computers in Human Behavior, 57, 153-159.
Sedgwick, P. (2014). Cross sectional studies: advantages and disadvantages. BMJ: British
Medical Journal (Online), 348.
Upton, J. (2013). Beck Depression Inventory (BDI). In Encyclopedia of Behavioral
Medicine (pp. 178-179). Springer, New York, NY.
Woods, H. C., & Scott, H. (2016). # Sleepyteens: social media use in adolescence is
associated with poor sleep quality, anxiety, depression and low self-esteem. Journal
of Adolescence, 51, 41-49.