Egyptian Journal of Psychiatry

: 2021  |  Volume : 42  |  Issue : 1  |  Page : 50--56

Smartphone addiction among medical students in mansoura university

Shiamaa Eldesokey1, Zeinab Gomaa2, Yomna Sabri3, Abdel-Hady El-Gilany3, Mohamed Elwasify2,  
1 Department of Psychiatry, Port Said Psychiatry Hospital, Port Said
2 Department of Psychiatry, Mansoura University, Mansoura, Egypt
3 Department of Public Health & Preventive Medicine, Mansoura University, Mansoura, Egypt

Correspondence Address:
Mohamed Elwasify
Assistant Prof of Psychiatry and Addiction Medicine, Departement of Psychiatry, Mansoura University, Mansoura 35516


Background In the past few years, there has been a growing attention to smartphone addictions. Various studies conducted within the past decade have analyzed the harmful effects of smartphone overuse on university students including medical students. Objective This cross-sectional study on 780 students estimated the prevalence of smartphone addiction and its associated factors in medical students of Mansoura University, Egypt. Patients and methods A self-administered questionnaire was completed to gather data about Problematic Use of Mobile Phones scale, sociodemographic characteristics, Depression Anxiety Stress Scale 21, Insomnia Severity Index, and feeling of loneliness (UCLA) questionnaire. Results The overall prevalence of smartphone addiction was 53.6%. The significant independent predictors of smartphone addiction are studying less than or equal to 4 h [adjusted odds ratio (AOR)=1.6], mild/moderate and severe/extreme severe depression (AOR=2.5 and 3.4, respectively), and severe/extreme severe stress (AOR=2.1). Conclusion Smartphone addiction is common among medical students and closely related to psychological problems.

How to cite this article:
Eldesokey S, Gomaa Z, Sabri Y, El-Gilany AH, Elwasify M. Smartphone addiction among medical students in mansoura university.Egypt J Psychiatr 2021;42:50-56

How to cite this URL:
Eldesokey S, Gomaa Z, Sabri Y, El-Gilany AH, Elwasify M. Smartphone addiction among medical students in mansoura university. Egypt J Psychiatr [serial online] 2021 [cited 2021 Jun 13 ];42:50-56
Available from:

Full Text


According to Oxford Dictionaries (2016), smartphone is defined as a mobile phone that performs many of a computer’s tasks. It usually has a touchscreen interface, internet access, and an operating system that can run downloaded applications.

They have become an increasingly important component of the daily life because of their many advantages (Alosaimi et al., 2016). Smartphone is helpful for medical students to search for data about a specific disease or drug from the bedside of the patient, communicate to organize a learning session, view images, listen to podcasts, and download textbooks. Most of them used applications related to medicine that assisted them in clinical environment (Robinson et al., 2013).

The idea of smartphones addiction is not proposed in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders as nonsubstance-related disorder. Smartphone addiction involves four main criterions: incontrollable mobile use; behaviors like frequent checking for various applications; tolerance; and withdrawal and impairment either academic or functional or social (Lin et al., 2016).

There were some suggestions that smartphone misuse is linked to several psychological and behavioral problems such as depression, anxiety, sleep disturbance, headaches, decreased concentration, memory loss, hearing loss, and fatigue (Khan, 2008; Thomee et al., 2011; Machell et al., 2015).

Smartphones are becoming increasingly indispensable in everyday life for most undergraduates with increasing the problem of addiction during adolescence and early adulthood. Despite this, there is a dearth of knowledge about smartphone addiction among medical students in Egypt.


The current study aims to estimate the prevalence of smartphone addiction and its associated factors among the undergraduate medical students in Faculty of Medicine, Mansoura University, Egypt.

 Patients and methods

Study design and setting

This is a cross-sectional observational study directed during year 2018 in Faculty of Medicine of Mansoura University, Egypt. The medical schools in Egypt offer a 6-year-long degree program divided into three academic years followed by three clinical years.

This study was approved by the IRB of Faculty of Medicine, Mansoura University (Code Number: 17.12.68). A written informed consent was obtained from participants prior to the admission after explaining the purpose of the study.

Target population is Egyptian medical students in all educational years with no history of psychiatric diseases. Sample size was calculated using the following formula: n=Z1-α2p (1-p)/d2, where p is the expected prevalence of smartphone addiction, which among Lebanese students was 44.6%, Z1-α/2=1.96, and d=precision (margin of error)=0.05 (Lwanga and Lemeshow, 1991; Hawi and Samaha, 2016). The sample size of 380 was multiplied by 2 to compensate for the design defect for cluster sampling method, and the total sample size is 760. The questionnaire was distributed to 842 students, and 780 (92.6% response rate) returned completed forms. Those who did not complete the questionnaires refused to participate in the study. Three clusters were selected from each educational year by systematic random samples from the list of sections or rounds. The questionnaire was distributed and collected during the class times.


An Arabic self-administered questionnaire was used to collect the sociodemographic data, for example, age, sex, residence during the academic year, special habits (excess caffeine drinks and cigarette smoking), substance use, physical exercise, marital status, working while studying, number of studying hours/day, the year of study, and the academic achievement.

Smartphone addiction was assessed by Problematic Use of Mobile Phones (PUMP) scale (Merlo et al., 2013). It includes 20 items that evaluate mobile phone use built on the Diagnostic and Statistical Manual of Mental Disorders-criteria for substance use disorder such as tolerance, withdrawal, craving, social problems, facts about physical risk due to the usage of mobile phones, physical and psychological problems, using for longer time than planned, great deal of time spent, activities given up or reduced, and failure of fulfilling role duties. The respondents responded each PUMP scale question on a Likert-type scale, where strongly disagree corresponds to one and strongly agree corresponds to 5. The total score ranges from 20 (lowest score) to 100 (highest score). The Arabic version used in this research was translated by Alosaimi et al. (2016). There is no operational characteristic to identify the cutoff point of the PUMP scale. In our study, smartphone addiction is defined by having PUMP score more than or equal to mean PUMP score of studied students (67.9).

Depression Anxiety Stress Scale 21 (Lovibond and Lovibond, 1995) was used to evaluate symptoms of anxiety, stress and depression, which is a summarized 21-item version of the full Depression Anxiety Stress Scale, which primarily consisted of 42 items. The three scales contain seven items that assess the dimensions of depression, anxiety, and stress. The severity ratings are grounded on percentile scores, with 0–78 classified as normal, 78–87 as mild, 87–95 as moderate, 95–98 as severe, and 98–100 as extremely severe. The Arabic version of the scale was used in the current study (Moussa et al., 2001).

Insomnia was assessed by Insomnia Severity Index (Morin et al., 2011). It is a seven-item self-reported questionnaire determining the nature, severity, and effect of insomnia over one month. The dimensions estimated include sleep onset, sleep maintenance, early morning awakening problems, sleep dissatisfaction, interference of sleep difficulties with daytime functioning, noticeability of sleep problems by others, and distress caused by the sleep difficulties. A five-point Likert scale is used to rate each item, yielding a total score ranging from 0 to 28. The total score is interpreted as follows: absence of insomnia (0–7); subthreshold insomnia (8–14); moderate insomnia (15–21); and severe insomnia (22–28). The Arabic Version of the scale was used in the present study (Suleiman and Yates, 2011).

Feeling of loneliness was assessed by UCLA Loneliness Scale (Rusell, 1996). It is a 20-item Likert-type scale in which responses ranged from 1 (never) to 4 (always). The scale includes nine positively worded items (1, 5, 6, 9, 10, 15, 16, 19, and 20) and 11 negatively worded items (2, 3, 4, 7, 8, 11, 12, 13, 14, 17, and 18) randomly disturbed throughout the scale. The degree of loneliness was categorized as mild (20–39), moderate (40–59), and severe (60–80). The Arabic version used for this study was translated by Daswqee (1998).

Statistical analysis

Data are coded, processed, and analyzed using SPSS software package, version 16 (SPSS Inc., Released 2007. SPSS for Windows, Version 16.0. Chicago, SPSS Inc.). Categorical data were presented as number and percentage. Non-normally distributed continuous data were described as median and minimum and maximum. χ2 Fisher’s exact test was used to test significance of categorical data as appropriate. Crude odds ratios (COR) with their 95% confidence intervals were calculated. Variables significantly associated with smartphone addiction in bivariate analysis were entered in stepwise logistic regression analysis using Forward Wald method. Adjusted odds ratios (AOR) with 95% confidence interval were calculated. Statistical significance was defined as P value less than or equal to 0.05.


[Table 1] shows that the overall prevalence of smartphone addiction is 53.6%. The factors associated with mobile phone addiction are physical exercise (COR=1.8), studying for 4 h or less (COR=1.8), mild/moderate and severe/extreme severe depression (COR=3.0 and 6.1, respectively), mild/moderate and severe/extreme severe anxiety (COR=2.0 and 5.2, respectively), mild/moderate and severe/extreme severe stress (COR=2.4 and 4.6, respectively), mild/moderate and severe/extreme severe insomnia (COR=2.6 and 4.8, respectively), and severe/extreme severe loneliness (COR=1.6).{Table 1}

The significant independent predictors of smartphone addiction are studying less than or equal to 4 h (AOR=1.6), mild/moderate and severe/extreme severe depression (AOR=2.5 and 3.4, respectively), and severe/extreme severe stress (AOR=2.1) ([Table 2]).{Table 2}

[Table 3] shows that 81.8% of students used smartphone for more than 3 years, 6.8% spent less than 8 h using smartphone, and 57.8% uses less than four applications. Smartphone was mainly used for social interacting purposes (66.8%), watching news (43.7%), academic tasks (39.2%), games (28.5%), education (22.4%), and scientific purposes (21).{Table 3}


Smartphone use is nearly universal among college students, especially medical ones. Extreme use of smartphones has departed up to a degree, classified as addictive behaviors. The topic has shown the consideration of many researchers.In this study, the prevalence of smartphone addiction was 53.6%, which is higher than rates reported in previous studies: 36.5 and 27.2% in Saudi Arabia (Alhazmi, 2018; Albursan et al., 2019), 44.6% in Lebanon (Hawi and Samaha, 2016), 31.7% in Tunis (Khan, 2008), 9.3% in Iran (Yahyazadeh et al., 2017), 10% in Belarus (Szpakow et al., 2011), 24.8% in South Korea (Kwon et al., 2013), 12.8% (Spain), 21.5% Belgium (Lopez-Fernandez et al., 2017), 16.9% in Switzerland (Haug et al., 2015), 29.8% in China (Chen et al., 2017), 31.3% in India (Nikhita et al., 2015), 17.3% in Sudan, and 8.6% in Yemen (Albursan et al., 2019). However, much higher rates were reported in India (85.40%) (Sethuraman et al., 2018) and Jordan (59.8%) (Albursan et al., 2019).

The reason for high smartphone usage in the present study could be owing to the lack of other sources of outdoor entertainment among medical students owing to preoccupation with studying and academic activities and finding smartphones as their only spring of entertainment and a way to relieve stress and anxiety. Adding to that, there is a constant obsession among young population about taking selfie and posting them on social media, which is easily available on smartphone, besides various applications of chatting, gaming, and social interaction (Sethuraman et al., 2018). The wide variation of smartphone addiction between different studies could be attributed to different sample sizes, different population characteristics (age, sex, level of education, and culture), the extent of 4 G Wi-Fi coverage, and use of different tools for assessing levels of smartphone addiction. Another outlook may be universal variation in addiction liability, something which can be related to universal differences in average personality shape (Meisenberg, 2015).

By logistic regression, studying for less than or equal to 4 h is an independent significant risk factor for smartphone addiction. Smartphone addiction and preoccupation with the smartphone result in the neglect of other assignments and tasks (Walsh et al., 2007). Many studies correlated smartphone usage with the decrease in academic achievement (Kubey et al., 2001). Some studies emphasized the positive role of smartphones in proceeding students’ learning (Markett et al., 2006; Cheon et al., 2012).

In the present study, mild/moderate and severe/extreme severe depression (AOR=2.5 and 3.4, respectively) and severe/extreme severe stress (AOR=2.1) are significant independent predictors of smartphone addiction. There was a strong positive relationship between smartphone addiction and depression (Yen et al., 2009; Alhassan, 2015; Matar and Jaalouk, 2017). Previous studies reported that students who used their smartphones more frequently had higher depression and anxiety scores (Hwang et al., 2012; Demirci et al., 2015; Tamura et al., 2017).

One of the influencing reasons of smartphone addiction is increased stress levels trailed by a decrease in restraint, which eventually leads to the over-practice of smartphones (Chen et al., 2017). Two previous studies reported no association between having smartphone and symptoms of depression and concluded that addiction is linked to self-control rather than the possession of the phone itself (Lemola et al., 2015; Choi et al., 2015).

Smartphones may source anxiety and depression, and extreme use of them may alter the biological clock, root sleep disorders, and cognitive, emotional, and mental symptoms. Furthermore, adolescents often wake up owing to the announcements they accept, and failure to sleep at night reduces the synthesis of melatonin (Cain and Gradisar, 2010).

In the current study, 65.8% of participants spent less than 8 h/day using smartphone, and 66.8% of participants used smartphone for social interacting. These findings are in a close agreement with the pattern of smartphone use in other countries. In Saudi Arabia, 34.2% of participants spent more than 8 h daily (Alosaimi et al., 2016) and 55.8% of students used smartphone more than 5 h daily (Alhazmi, 2018). In Malaysia, 65.9% of students use more than 3 h/day (Nikmat, 2018) and 30% used smartphones more than 3 h in India (Kurugodiyavar, 2018).

Javid et al. (2011) examined the consequences of mobile phone on the academic achievement of university students. Utmost of the students answered that they used mobile devices to chat with their instructors and colleagues to discuss materials related to their study. They also used the mobile devices to segment valuable information with their classmates and to check a dictionary for educational aims. Nevertheless, they settled that the mobile phone wastes their time and money.

More research should be done among all sectors of the society besides teaching programs, which may help the students understand the risk of smartphone addiction and how to manage their stress related to the burden of the study.

Strengths and limitations of the study

To the best of our knowledge, this is one of the few studies done that have evaluated the prevalence of smartphone addiction among medical students, and it detects various associated factors in the form of depression, anxiety, stress, loneliness, insomnia, and other sociodemographic factors in Egypt. This study has some limitations: (a) it was done in one medical college, so the results cannot be generalized to all medical students nationwide; (b) reporting and recall biases cannot be removed, as the students can manipulate the information; (c) there was no comparison group using the same forms or from nonmedical students; (d) the cross-sectional design did not determine whether depression–stress–anxiety–insomnia–feeling of loneliness were causes of smartphone addiction or whether smartphone dependence may be a coping strategy for students to get rid of these problems; and (e) arbitrary cutoff point was taken to define smartphone addiction, as no validated cutoff point is available.


Smartphone addiction is prevalent among undergraduate medical students and closely related to psychological problems.


Authors’ contributions: S.E.: data collection and drafting the manuscript. Z.G.: supervision and coordination of all research activities, revision of final draft for important intellectual contents. Y.S.: revision of results, manuscript for intellectual contents. A.-H.E.-G.: research design, data analysis and interpretation. M.E.: conception of research idea, revision of results, manuscript for intellectual contents. All authors read the final manuscript and agreed about its contents. All authors have read and approved the manuscript.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.



1Albursan IS, Al Qudah MF, Dutton E, Hashem Hassan EMA, Attallah Bakhiet SF, Alfnan AA, Aljomaa SS, Hammad HI (2019). National, sex and academic discipline difference in smartphone addiction: a study of students in Jordan, Saudi Arabia, Yemen and Sudan. Community Ment Health J 55:825–830.
2Alhassan M (2015). Smartphone usage among medical students in Saudi Arabia. Int J Sci Res 6:2227–2229.
3Alhazmi A (2018). Prevalence and factors associated with smartphone addiction among medical students at King Abdulaziz University, Jeddah. Pak J Med Sci 34:984–988.
4Alosaimi FD, Alyahya H, Alshahwan H, Al Mahyijari N, Shaik SA (2016). Smartphone addiction among university students in Riyadh, Saudi Arabia. Saudi Med J 37:675–683.
5Cain N, Gradisar M (2010). Electronic media use and sleep in school-aged children and adolescents: a review. Sleep Med 11:735–742.
6Chen B, Liu F, Ding S, Ying X, Wang L, Wen Y (2017). Gender differences in factors associated with smartphone addiction: a cross-sectional study among medical college students. BMC Psychiatry 17:1–10.
7Cheon J, Lee S, Crooks SM, Song J (2012). An investigation of mobile learningreadiness in higher education based on the theory of planned behavior. Computers Educ 59:1054–1064.
8Choi S, Kim D, Choi J, Ahn H, Choi E, Song W (2015). Comparison of riskand protective factors associated with smartphone addiction and internet addiction. J Behav Addict 4:308–314.
9Daswqee M (1998). UCLA loneliness scale: translation and adaptation. Cairo, Egypt: Al-Anglo Almesria Publishing.
10Demirci K, Akgonul M, Akpinar A (2015). Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J Behav Addict 4:85–92.
11Haug S, Castro RP, Kwon M, Filler A, Kowatsch T, Schaub MT (2015). Smartphone use and smartphone addiction among young people in Switzerland. J Behav Addict 4:299–307.
12Hawi N, Samaha M (2016). To excel or not to excel: strong evidence on the adverse effect of smartphone addiction on academic performance. Computers Educ 98:81–89.
13Hwang KH, Yoo YS, Cho OH (2012). Smartphone overuse and upper extremity pain, anxiety, depression, and interpersonal relationships among college students. J Korea Contents Assoc 12:365–375.
14Javid M, Malik MA, Gujjar AA (2011). Mobile phone culture and its psychological impacts on students’ learning at the university level. Lang India 11:416–422.
15Khan MM (2008). Adverse effects of excessive mobile phone use. Int J Occup Med Environ Health 21:289–293.
16Kubey RW, Lavin MJ, Barrows JR (2001). Internet use and collegiate academicperformance decrements: early findings. J Commun 51:366–382.
17Kurugodiyavar MD (2018). Impact of smartphone use on quality of sleep among medical students. Int J Comm Med Public Health 5:101–109.
18Kwon M, Lee JY, Won WY, Park JW, Min JA, Hahn C et al. (2013). Development and validation of a smartphone addiction scale (SAS). PLoS ONE 8:e56936.
19Lemola S, Perkinson-Gloor N, Brand S, Dewald-Kaufmann JF, Grob A (2015). Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. J Youth Adolesc 44:405–418.
20Lin YH, Chiang CL, Lin PH, Chang LR, Ko CH, Lee YH, Lin SH (2016). Proposed diagnostic criteria for Smartphone addiction. PLoS ONE 11:e163010.
21Lopez-Fernandez O, Kuss DJ, Romo L (2017). Self-reported dependence on mobile phones in young adults: a European cross-cultural empirical survey. J Behav Addict 6:168–177.
22Lovibond SH, Lovibond PF (1995). Manual for the depression anxiety stress. manual for the depression anxiety stress scales. 2nd ed. Sydney: PsychologyFoundation of Australia.
23Lwanga S, Lemeshow S (1991). Sample size determination in health studies. A practical manual. Geneva: WHO.
24Machell KA, Goodman FR, Kashdan TB (2015). Experiential avoidance andwellbeing: daily diary analysis. Cogn Emot 29:351–359.
25Markett C, Sánchez IA, Weber S, Tangney B (2006). Using short message service to encourage interactivity in the classroom. Comput Educ 46:280–293.
26Matar BJ, Jaalouk D (2017). Depression, anxiety, and smartphoneaddiction in university students − a cross sectional study. PLoS ONE 12:e0182239.
27Meisenberg G (2015). Do we have valid country-level measures of personality? Mankind Quart 55:360–382.
28Merlo LJ, Stone AM, Bibbey A (2013). Measuring problematic mobile phone use: development and preliminary Psychometric properties of the PUMP scale. J Addict 912807.
29Morin CM, Belleville G, Belanger L, Ivers H (2011). The Insomnia Severity Index: psychometric indicators to detect insomnia cases and evaluate treatment response. Sleep 34:601–608.
30Moussa MT, Lovibond PF, Laube R (2001). Psychometric properties of an Arabic version of the Depression Anxiety Stress Scales (DASS21). Sydney: New South Wales Transcultural Mental Health Centre, Cumberland Hospital.
31Nikhita CS, Jadhav PR, Ajinkya SA (2015). Prevalence of mobile phone dependence in secondary school adolescents. J Clin Diagn Res 11:6–9.
32Nikmat WA (2018). The use and addiction to smart phones among medical students and staffs in a public university in Malaysia. J Psychiatry 19:2231–7805.
33Oxford Dictionaries. Smartphone [Updated 2016; cited 2015 Sep 26]. Available from:
34Robinson T, Cronin T, Ibrahim H, Jinks M, Molitor T, Newman J, Shapiro J (2013). Smartphone use and acceptability among clinical medical students: a questionnaire-based study. J Med Syst 37:9936.
35Rusell D (1996). UCLA loneliness scale (version 3): reliability, validity and factor structure. J Philosophical Stud 20:473–497.
36Sethuraman AR, Rao S, Charlette L, Thatkar P, Vincent V (2018). Smartphone addiction among medical college students in the Andaman and Nicobar Islands. Int J Comm Med Public Health 5:4273–4277.
37Suleiman KH, Yates BC (2011). Translating the insomnia severity index intoArabic. J Nurs Scholars 43:49–53.
38Szpakow A, Stryzhak A, Prokopowicz W (2011). Evaluation of threat of mobile phone addiction among Belarusian University students. Prog Health Sci 1:96–101.
39Tamura H, Nishida T, Tsuji A, Sakakibara H (2017). Association between excessive use of mobile phone and insomnia and depression among Japanese adolescents. Int J Environ Res Public Health 14:701.
40Thomee S, Harenstam A, Hagberg M (2011). Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults − a prospective cohort study. BMC Public Health 11:66.
41Walsh S, White K, Young R (2007). Over-connected? Qualitative exploration of the relationship between Australian youth and their mobile phones. J Adolesc 31:77–92.
42Yahyazadeh S, Fallahi-Khoshknab M, Norouzi K, Dalvandi A (2017). The prevalence of smart phone addiction among students in medical sciences universities in Tehran 2016. J Nurs Midwif 26:1–9.
43Yen CF, Tang TC, Yen JY (2009). Symptoms of problematic cellular phone use, functional impairment and its association with depression among adolescents in Southern Taiwan. J Adolesc 32:863–873.