INTRODUCTION

Smartphones are popular mobile devices, usually with a touchscreen, containing a large number of features allowing for communication, accessing the internet and social networks, messaging, video, games, taking photographs, using multimedia, and navigation. According to a multi-center study that also included Turkey, the smartphone is the electronic device with the largest ownership at a rate of 92% (1). Among the European countries included in the study, the one with the greatest use of smartphones turned out to be Turkey, where users on average check their phone 78 times per day (European mean: 48), 79% check their phone in the first 15 minutes after waking up (European mean: 62%), 72% check their phone in the last 15 minutes before going to be (European mean: 53%), and 85% check their phones in bed during the night for whatever reason (European mean: 48%) (1). These results demonstrate that smartphones, with their main characteristic being internet-based applications, can lead to excessive and uncontrolled use with detrimental outcomes especially in a nation like Turkey where the young make up a large share of the population (2). Though the smartphone offers great convenience for our daily lives, its use may be problematic, the greatest problem being smartphone addiction, where smartphone users lose control over their consumption (3).

With the common use of technology and technological devices, pathological use has developed at the same time. There is a great number of studies in the literature proposing the diagnosis of smartphone addiction (4,5). Although there are evidence-based studies, smartphone addiction has not yet been included in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and there are no official instruments available to measure the condition. Defining behavioral addictions, including smartphone addiction, is actually difficult as it includes not only physical dependency but also involves social and psychological factors (6). Among the basic characteristics of behavioral addictions are the following: continuing to busy oneself with a certain behavior despite of negative effects, using this behavior to escape from reality or to create a feeling of euphoria, developing tolerance in the continuation of the behavior, sensing withdrawal symptoms in its absence, incurring interpersonal problems due to the incessant behavior, and relapse into it (7). Due to the problems it causes, smartphone addiction is becoming more of a public health issue every day. Thus, studies have shown negative effects of problematic smartphone use including a reduced quality of sleep (7,8), depression (8), anxiety (8), poor academic performance (9), problems in the recognition and expression of feelings (10), relational disharmony (10), and fatigue (7). While the increasing use of smartphones in daily life triggered a significant amount of research, we found that the number of studies dealing with the causes of smartphone addiction in the Turkish university student population and their smartphone use profiles is very limited.

The aim of our study is to present smartphone use profiles in the Turkish university student population and the purposes of phone use. In addition, we aim to determine which purposes of smartphone use increase the addiction risk and if there is a difference in purpose between the sexes.

METHOD

This cross-sectional study was carried out in 2017 at the Neuropsychiatry Center of the Health & Training Academy (HTA). Enrolled in the study were students from various universities in Istanbul (Health Sciences University, Istanbul University, Dogus University, and Halic University) who had been using a smartphone for at least 1 year. Individuals with any diagnosis of a psychiatric disease or outside the age bracket of 18-65 years were not included in the study. We invited 3620 students to participate, of whom 1968 refused to be enrolled and 187 participants had to be excluded because they had not completed the survey or filled in the form only partly. In the end, 1465 university students in formal education who volunteered to participate were included in the study (Figure 1). Approval from the local ethics committee was obtained before beginning the study, and all volunteers gave their consent before completing the survey. Our study conforms to Helsinki standards.

Data were collected with standardized, anonymous self-report survey forms. All participants were surveyed online with a sociodemographic form requesting personal information, smartphone habits, educational information and details about smartphone use. The participants were invited through a web link. To prevent multiple participation, the mail addresses of all participants were recorded. To determine their smartphone addiction status, all volunteers were administered the Smartphone Addiction Scale–Short Version (SAS-SV) and a form prepared by the researchers for this study in line with the literature to examine the purposes of smartphone use. After the survey, the data were recorded as regular data sets and analyzed.

Smartphone addiction was measured with the SAS-SV, an instrument consisting of 10 items rated on a Likert-type scale from 1 (completely disagree) to 6 (completely agree), resulting in a total score between 10 and 60. High scores are correlated with smartphone addiction during the past year (4). A validity and reliability study for the Turkish version was carried out by Noyan et al. (11) finding a Cronbach’s alpha coefficient of 0.867. Kwon et al. (4) reported cutoff points for smartphone addiction of ≥31 for males and ≥33 for females. These values were used for smartphone addiction.

In addition to the SAS-SV, questions were asked about demographic information, age, sex, education level, living circumstances, and smartphone use. After a review of the literature, participants’ smartphone use profiles were investigated determining the most common purposes for smartphone use (5,12-14). The participating university students’ family income was classified according to data from the Turkish Statistical Institute as “low” if below the hunger limit, “intermediate” if between hunger limit and poverty line, and “high” if above the poverty line. Participants were requested to rank their phone bills as “low,” “intermediate,” “high,” or “very high” according to their own judgment.

Statistical Analysis

Data analysis was performed with SPSS version 20.0 (Chicago IL, USA). Study participants’ sociodemographic characteristics (sex, age, education level) were analyzed descriptively; numerical variables were presented as mean±standard deviation, categorical variables as frequency and percentage. To establish if parametric or non-parametric tests could be used, data were first tested for normal distribution. For variables conforming to normal distribution, Student’s t-test was used. To compare differences between categorical variables, chi-square test was applied. Logistic regression analysis was used between multiple variables. A value of p≤0.05 was accepted as statistically significant.

RESULTS

The sociodemographic and smartphone use characteristics of the 1465 students enrolled in the study are presented in Table 1. Of these students, 861 (58.8%) were female and 604 (41.2%) male. Statistically significant differences between the sexes were found for the following sociodemographic characteristics: age (p<0.001), height (p<0.001), weight (p<0.001), Body Mass Index (p<0.001), faculty of study (p<0.001), living circumstances (p<0.001), and monthly phone bill (p<0.05). Income level, frequency of changing phones, and phone addiction were not statistically significant (p>0.05).

Regarding students’ purpose for using the smartphone, a statistically significant difference between female and male participants was found for playing games (p<0.001), meeting new friends (p<0.001), and following the news (p<0.05) (Table 2). For the other purposes (social media use, use for studying/academic purpose, messaging, entertainment [watching series, movies, clips], and shopping), there was no statistically significant difference (p>0.05). The effect of phone use profiles on the SAS-SV scores is presented in Table 3. In students using smartphones mainly “for social media” and “to meet new friends,” SAS-SV scores were statistically significantly elevated for females (p<0.001), males (p<0.001), and the total sample (p<0.001). For the profiles “use for studying/academic purpose” and “messaging,” SAS-SV scores were statistically significant for the total sample and for female students (p≤0.05), but not significant for male students (p>0.05). For the “playing games” profile, SAS-SV scores were statistically significant only for male students (p≤0.05), while for the total sample and female students, scores were not significant (p>0.05). SAS-SV scores for “shopping” were statistically significant overall (p≤0.05), but SAS-SV scores for “entertainment” use turned out not to be statistically significant (p>0.05).

Table 4 presents the effect of smartphone use purposes on addiction for female and male students and for the whole sample, using logistic regression. The results show that for all participants, “social media use” and “meeting new friends” profiles increase the risk of smartphone addiction the most, while “shopping” has the lowest effect (p≤0.05). In female students, the smartphone addiction risk is increased by “social media use,” “studying/academic purpose,” “playing games,” “meeting new friends,” and “following the news” (p≤0.05), while in male students, “social media use,” “meeting new friends,” and “entertainment” increase the addiction risk (p≤0.05).

DISCUSSION

Our study examined smartphone use characteristics and purposes that may predict smartphone addiction in university students. Our findings indicate that using phones for “social media use” and “meeting new friends” increases the smartphone addiction risk, while “use for studying/academic purpose” and “following the news” reduce that risk. Another important finding of our study is the fact that male students use their smartphone more for “playing games,” “meeting new friends,” and “following the news” than their female counterparts. To our knowledge, our study is the first one to examine the correlation between smartphone use profiles and smartphone addiction in a population of university students.

According to a behaviorist approach, if a behavior leads to gratification and/or relief and/or if it detracts from negative situations like tension or distress, an individual will continue with this behavior or increase it to feel gratification/pleasure and/or avoid negative situations (15). From this perspective, a person might use the smartphone to avoid a negative environment and/or to feel pleasure. On the other hand, it is interesting to ask if the addictive effect of the ever-present smartphone lies in the device itself, in the purposes for its use (games, gambling, social media, messaging), or in its applications.

Studies on smartphone addiction found social media use, playing games, and use for entertainment to be predictive of smartphone addiction, while academic use was not found to be predictive (12,13). In line with earlier work, our study found that the purposes “social media use” and “meeting new friends” increased the risk of smartphone addiction in the overall population, while “use for studying/academic purpose” and “following the news” reduced the risk of addiction in the whole population. In addition, our study showed that using the smartphone to “play games” increased the addiction risk in female students, while smartphone use for entertainment increased the risk in male students. We believe that these findings are first in the literature. Our results show that the main modality of using smartphones is to access social media, and this use is the one that leads to the greatest increase in the addiction risk. In addition, the small size of smartphone screens may reduce its usefulness and enjoyment for the intended purpose. Therefore, there may be a preference for devices with a bigger screen for playing games, academic use, shopping, and entertainment (16).

Chen et al. (17) have shown that men and women use smartphones for different purposes: while men more often play games and seek entertainment, women use the phone more for messaging and social media. According to our results, males use the smartphone more for “playing games,” “meeting new friends,” and “following the news” than females. In contrast to the literature, we found no difference between female and male students for “social media use,” “entertainment,” and “messaging.” The most likely explanation for this divergent result could be the cultural difference between the samples, as in Turkey male students are using social media and communication as much as females do. In addition, our study also found no difference between female and male students for “studying/academic purpose” and “entertainment.”

Despite the large study sample, we need to assess the study in the light of certain limitations. First of all, the participants are all university students and thus do not represent the population as a whole. We need studies with participants from different age groups and with a different education background. Secondly, as all measurements and data were collected using self-report instruments, data are open to fudging. Thirdly, a cross-sectional design is not the best method to assess causal relations. Fourth, the distinction between different purposes for smartphone use may not be so clear. For example, some types of social media provide entertainment content or allow playing games. Future studies should take possible overlap between content categories into account. On the other hand, the literature in this field is not yet rich enough. Finally, we used an SAS-SV cutoff point established in a Korean sample for the diagnosis of smartphone addiction, which is another limitation of our study.

Considering the high prevalence of smartphone addiction among university students found in our study, we suggest that smartphone addiction is a public health issue in Turkey. This study considers the correlation between certain purposes for the use of smartphones and smartphone addiction. We found that social media use and meeting new friends increased the students’ addiction risks the most, while “use for studying/academic purpose” and “following the news” reduced the addiction risk. As our study emphasized sex-differences in smartphone addiction, we suggest that a preventive and interventional strategy to reduce this behavioral problem should be multifactorial and sex-specific.

Ethics Committee Approval: Approval from the local ethics committee was obtained before beginning the study, and all volunteers gave their consent before completing the survey.

Informed Consent: Written informed consent was obtained from the patient for the publication of the case report and the accompanying images.

Peer-review: Externally peer-reviewed.

Conflict of Interest: No conflict of interest.

Financial Disclosure: The authors declare no financial support.

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