The results of a rapid literature search in MEDLINE and MEDLINE in progress suggest that there is a small number of primary research studies regarding the potential risk of mobile phone use on mental health, and consequently, there is insufficient foundation of evidence in this area. Therefore, this study takes one step towards shedding light on this complex phenomenon. The aim of this research was to explore the manner and intensity of the use of mobile phones and examine its effects—especially the effects of long-term mobile phone use—on certain mental health aspects of university students in Serbia and Italy, by measuring levels of depression, anxiety, and stress.
All of the participants were assessed by using a questionnaire, and the total number of responding participants was , of whom 42 were rejected due to incomplete responses. Thus, the study ultimately included randomly selected university students males and females. The students reported their socioeconomic characteristics, lifestyle habits, attitudes, and health assessment using self-administered questionnaires, which also provided answers to questions about the manner, purpose, and intensity of mobile phone use.
Symptoms of depression, anxiety, and stress were assessed using the Depression Anxiety Stress Scale DASS 42 , a device for measuring psychological health [ 17 , 18 ]. The survey was performed in classrooms by trained assistants interviewers and was intended to last a maximum of 20 min, including the time needed for instructions. The DASS device is a set of three self-reporting scales designed to measure the negative emotional states of depression, anxiety, and stress.
Each of the three scales contains 14 items, divided into the subscales of 2—5 items with similar content.
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DASS is designed to be used for further defining, understanding, and measuring all present and clinically significant emotional states in the examinees [ 17 , 18 ]. The students were asked to mark from 0 none to 3 mostly or almost always the extent to which they have experienced each of the listed conditions during the previous week. The score results of depression, anxiety, and stress were calculated by adding the points for each relevant scale. The result was then calculated for every student and for each of the subscales, according to the score matrix, and then evaluated as per the severity-rating index below Table 1.
The means for each scale are 6. The frequency of calls conversations per day How many calls on average do you make and receive a day with your mobile phone? The frequency of SMSs per day How many text messages do you send or receive on your mobile phone?
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The subjects also reported their time spent on the internet, time spent using various applications, and time spent playing games If you use other mobile phone functions, how much time do you spend using those functions? The distance of the mobile phone during sleep Is your mobile phone less than 1 m away while you sleep? All of the data were entered into Excel spreadsheets Microsoft Office , Microsoft, Redmond, DC, USA by several teams each consisting of two people, whereby cross-checking was done for every given survey.
The statistical analysis was performed using the SPSS The research results were presented in tables. The statistical analysis of the data included the application of descriptive tests and analytical parametric tests, as well as binary logistic regression tests and correlation tests. The descriptive statistics were performed to report the analysis of the data that were presented as mean and standard deviations. The categorical variables were shown as frequency and percentages.
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The independent t -test was used to compare the parametric variables between the genders. Pearson and Spearman correlations were used to determine the strength of the relationships between the examined variables. The study procedures were carried out in accordance with the Declaration of Helsinki. All subjects were informed about the study, and all provided informed consent.
The study included participants, male The mean value of the year of study was 2. The t -tests did not show any significant differences between students of both genders in either of these two values. There were students from Serbia, out of whom Out of the total number, students Moreover, of them Headphones were not used by Health issues were reported by 67 8.
In addition, students The mobile phone was kept at a distance of less than 1 m during sleep by Some other characteristics of the surveyed students are shown in Table 2. The associations between some characteristics of the surveyed students and their habits related to the use of mobile phones were examined by a Pearson correlation coefficient i.
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Preliminary analyses were conducted in order to check the assumptions of normality, linearity, and homogeneity of the variants. The following correlations were calculated Table 3. The higher amount of weekly allowance is correlated with more frequent calls, more time spent using mobile phones daily, a greater number of SMSs, and more frequent use of the internet, apps, and games. Those students who go to sleep later make more phone calls during the day, while physically more active students exchange fewer messages. Also, later sleep time is correlated with more internet browsing and longer hours of sleep with more games played Table 3.
Moderate, severe, or extremely severe levels of depression symptoms were reported by students Direct logistic regression was conducted in order to estimate the effects of several factors on the probability that the students would respond positively to questions about depression, anxiety, or stress. This indicates that the model distinguishes those respondents who are from those who are not sorted, so that they have some of the symptoms. The model explains between 6.
The assumptions of collinearity and singularity were satisfied, and non-typical points were also checked. As Table 5 shows, eight independent variables provided a unique statistically significant contribution to some of three presented models gender, year of study, number of calls per day, time spent on the phone per day, number of SMSs per day, distance of mobile phone during sleep, browsing the internet, and playing games.
This shows that male students responded that they have symptoms of depression 1. This shows that students who keep their mobile phones less than 1 m away while they slept reply 1. The country of origin Italy and Serbia played no part in any of the cases. The study findings suggest that a stronger predictor of depression among students in both countries is male gender.
Those results are not in line with the studies in the general population, even with some prospective studies, in which female gender was the risk factor for depression [ 19 , 20 ]. In addition, we found a pattern that a depressed male student makes fewer calls and browses the internet less frequently on the mobile phone and sends more SMSs.
In general, our findings confirm the results obtained by several studies reporting on the relationship between mobile phone use and depression symptoms [ 21 , 22 , 23 , 24 , 25 ].
However, it is important to emphasize that some studies indicate that it is addiction to mobile phones in college-age students that leads to depression and anxiety, not the use of mobiles [ 21 , 25 ]. Isolating such effect from the multivariate background of underlying causes governing depression, which are probably mediated by several levels of hierarchical and time-dependent factors, is a challenging task, even for prospective studies.
Furthermore, we found similar patterns in mobile phone usage among students with anxiety symptoms. Namely, those students also send many more SMSs and spend less time browsing the internet, and in this case, they are students of younger age. However, those patterns in both depressed students and students with anxiety symptoms can be triggered by smaller monthly economic budgets rather than by the use of mobile phones itself.
Some explanatory studies found that students with higher anxiety are more likely to use mobile phones as a compensatory attachment target [ 26 ]. Therefore, the correlation between anxiety and mobile phone use from our study is still an important question for future prospective studies as well. Those patterns characteristic of depression and anxiety are not associated with stress levels, for which the most significant predictor is the physical distance of the phone from bed during sleep less than 1 m.
Those findings are in line with similar findings from other research [ 23 ]. Current recommendations suggest that the cell phone should be at least three feet away from the body during sleep [ 27 ]. Several studies used EEG measurements to estimate more precisely the impact of mobile phones on sleep quality [ 13 , 28 , 29 , 30 , 31 ]. The results are currently rather inconclusive or inconsistent, and one study even found that sleep quality improved due to mobile phones [ 28 ].
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A large German research study from suggested that the worst sleep quality can be found among a particular type of study participants, and those findings cannot be generalized for the overall population [ 32 ]. Despite the reported results, several studies found an alteration in the circadian system and even changes in cardiac rhythms caused by mobile phones during sleeping hours [ 33 ].
Whether there is a predication for such alterations or those effects are typical for the overall population is an open question. The pathogenesis of the described conditions is still not completely understood, but current studies underline their causal interdependence [ 34 ].
Different kinds of stress are risk factors for developing an anxiety disorder, and these can, in turn, cause or worsen sleep disorders. Similarly, stress can trigger depression, which can then be complicated by anxiety. Several animal and human basic research studies indicate the effects of using mobile phones on cognition and brain functions [ 35 , 36 , 37 ]. Our and similar research confirm the findings on the manifestation level of those complex conditions.