Logo-jrhs
J Res Health Sci. 16(1):46-50. Abolfazl Mohammadbeigi a, Rozita Absari b, Farzaneh Valizadeh c, Mohammadreza Saadati b, Soroush Sharifimoghadam b, Ali Ahmadi d, Mohsen MokhtariORCID logo, Hossein Ansari f, *
a Department of Epidemiology, School of Health, Health Policy and Promotion Research Center, Qom University Of Medical Sciences, Qom, Iran
b Student Research Committee, Qom University of Medical Sciences, Qom, Iran
c General Practitioner, Genetic Counselor, Deputy of Health, Mazandaran University of Medical Sciences, Babolsar, Iran
d Modeling in Health Research Center, Department of Epidemiology, School of Health, Shahrekord University of Medical Sciences, Shahrekord, Iran
e MSc of Epidemiology, Health Vic Chancellor, Arak University of Medical Sciences, Arakm, Iran
f Health Promotion Research Center, Department of Epidemiology and Biostatistics, Zahedan University of Medical Sciences, Zahedan, Iran
*Correspondence Hossein Ansari (PhD) Tel: +98 253 37842228 E-mail: ansarih88@gmail.com

Abstract

Background: Poor sleep quality is closely associated with lifestyle habits including use of mobile cell-phones. This study aimed to identify the relationship between sleep quality due to abuse in mobile cell-phones and engagement in social networks.

Methods: This cross-sectional study was conducted on 380 undergraduate students selected by proportional stratified sampling in Qom, Iran in 2015. Data were collected by two statndard questionnaire including Cell-Phone Over-Use Scale (COS) and Pittsburgh sleep quality questionnaire beside the status of usage in cell-phone social networks. T-test, chi-square, Pearson correlation coefficient and multivariate logistic regression were used in data analysis.

Results: The mean age of participants was 21.8 ±3.2 yr, 69.1% were female, and 11.7% were married. The mean of COS and sleep quality scores were 48.18 ±17.5 and 5.38 ±2.31, respectively. The prevalence of over-use of cell phone was 10.7% (CI 0.95; 8.8%, 12.6%) and the prevalence of poor sleep quality was 61.7% (CI 0.95; 57.1%, 66.3%). The mean of all aspects and total score of sleep quality showed a direct significant association by cell-phone addiction score except sleep duration score that was inversely. Based on multivariate analysis affected to cell-phone addiction, being male gender and studying in general physician level are the most important predictors of poor sleep quality.

Conclusions: Over use of internet and social networks via smart phones is related to poor sleep quality and quantity. Predefined sport programs, educational, cultural, and interesting entertainment are the essential needs for all medical students. These interventions are more important especially for male students who have longer educational.

Keywords: Sleep, Social Networks, Internet Addiction, Cell-Phone Over-Use, Mobile Addiction, Students

Copyright

© 2016 The Author(s)


Introduction

Currently, the mobile cell-phones changed as a common tool in the individual’s daily life because people can connect to the virtual networks constantly via internet and the mobile cell-phones not only a talk tool 1 .

The prevalence of Iranian mobile phone and internet usage in Iran increased to 85% and 35%, respectively. Moreover, 22% of Iranian users and 58% of teenagers connect to the internet by their smartphones or tablets 2 . Installing social networks applications besides login to internet social networks by cell-phones caused to an addictive behavior in mobile phone users. More than 500 million people were active members of Facebook in 2011, while according to statistics portal site, Facebook users at the beginning of 2015, was about 1.44 billion. Moreover, Viber and WhatsApp users estimated about 573 and 800 million, respectively and 55% to 82% of adolescents and adults use social networks, regularly. 3 Social networks enabled interactions between friends and family members via virtual communities based on common interests 3,4 . Social networks can have negative effects, such as anxiety, depression, dependence, and addiction 5-7 ,. Besides, it can disturb the sleep quality 8,9 due to the adverse effects of electromagnetic field emitted by mobile phones and reduced melatonin production 10,11 .

Mobile phone addiction or internet addiction is defined as excessive behavior in using technology tools such as smart phones, android applications or its enteritainments 12 . In addition, people suffer from internet addiction, like those addicted to drugs and alcohol, are faced to excessive problems such as academic problems, social and occupation 13,14 . However, in view of psychologists and sociologists, addictive use of the internet is identifying as behavior problem that affect the sleep quality 8,9,13,15 . Good quality sleep is essential for health and life quality in all people and is related to several factors including environmental factors, social life, general health status, and stress 1,16 . Todays, mobile phone is one of the important environmental factors that can affect the sleep quality if overused 1,10 . Moreover, the poor sleep quality is related to increase risk of physical and mental disorders and closely associated with lifestyle habits including excessive use of mobile cell-phones 10 . In addition, the effect of sleep quantity and quality on non-communicable diseases such as diabetes is showed 9,17 . However, the medical students experience remarkable stress during long period of study and are prone to sleep deprivation.

This study aimed to identify the relationship between sleep qualities due to overuse of mobile cell-phones and engagement in social networks.


Methods

This cross sectional study was conducted in a random sample of Qom University of Medical Sciences, Qom, Iran including 380 undergraduate students studying in term 2 or higher in January 2015. Proportional stratified sampling method was used in selecting of participated students and each college defined as strata.

Informed consent was taken from all participants and the study protocol was approved by Ethics Committee of Qom University of Medical Science.

Data were collected by two statndard questionnaire including Cell-Phone Over-Use Scale (COS) and Pittsburgh sleep quality questionnaire. In addition, another questionnaire was used to gather the demographic characteristics and the status of use from mobile cell-phone’s social networks.

Cell-Phone Over-Use Scale (COS)

The COS was used for mobile cell-phone addiction data 18 and validated by Iranian researchers 19,20 and passed the validity tests as well as reliability tests by more than 90%. This questionnaire including 17 items and each item scoring between 1 and 5 for each item in a Likert scale.

After computing the mobile cell-phone addiction score, subjects were categorized in three levels as higher 75 as over-use, 25 to 75 as normal and lower 25 as lower normal 18,20 .

Pittsburgh sleep quality questionnaire

Pittsburgh sleep quality questionnaire 21 was used as a standard tool to assess the sleep quality and quantity in students. The Persian version of this questionnaire is validated in some studies in Iranian people 22,23 . Nineteen individual items generate seven “component” scores including subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime dysfunction. The sum of scores for these seven components yields one global score as sleep quality score. In each component, the scores varied from 0 to 3 and the total score of questionnaire varied since 0 to 21 and higher scores indicating the worse sleep quality 21 . For each components 0 indicates that no sleep difficulty, 1; mild difficulty, 2 sever difficulty and 3 very sever difficulty in sleep. The total score of sleep quality was categorized based on the lower or higher 5 and labeled as normal and poor, respectively 21 .

Statistical analysis

After data gathering and calculation of mobile cell-phone addiction and sleep quality scores for each study subjects, data were analyzed in SPSS software (Chicago, IL, USA). Descriptive statistics including percent mean and standard deviation were used for central and deviation indices. T-test and analysis of variance were used for comparing the sleep quality score between participants based on demographic characteristics (gender, marital status, residency place, educational term) and who used/not used different type of social networks. Pearson correlation coefficient was used for assessing the correlation between cell-phone addiction score with sleep quality score and sleep quality components’ score. Multivariate logistic regression by backward stepwise was method used for adjusting the covariates effect. Goodness of fitness was checked by Likelihoods and Hosmer-lemshow statistics and significant level was considered by 0.05 error.


Results

The mean age of participants was 21.8 ±3.2 yr, 69.5% were female, and 11.7% were married. In addition, 49.6% were resident in university dormitories. The response rate was 95.5% (363/380). The mean cell-phone addiction score and sleep quality score was 48.18 ±17.5 and 5.38 ±2.31, respectively. The prevalence of over-use of cell-phone estimated was 10.7% (CI 0.95; 8.8%, 12.6%) and the prevalence of poor sleep quality was 61.7% (CI 0.95; 57.1%, 66.3%). 45.6% of study subjects reported that used more than two hours from web, daily. Table 1 shows the demographic and prevalence of usage from social network in mobile cell-phones. Accordingly, more than 58% of students use Viber and WhatsApp applications in smart cell-phones.

Table 1. Demographic and prevalence of usage from social network of cell-phone
Variables Number Percent
Age group (yr)
<196618.6
20-2114841.7
22-238724.5
≥245415.2
Educational semester
≤314848.7
4-610333.9
≥75317.4
Gender
Male11030.5
Female25169.5
Educational level
Bachelor 23665.4
General practitioner 12534.6
Marital status
Single 31888.3
Married 4512.7
Living
In dormitories17349.6
With family18852.4
SIM Card
One13336.9
Over one22863.1
Type of social network
Viber21158.1
WhatsApp 22160.9
Tango7320.1
Line12935.5
Instagram 10127.8
Others6217.1

Using Viber, WhatsApp, Line and Tango were significantly related to poor sleep quality (Table 2). Moreover, living in university dormitories was related to higher score that indicated poor sleep quality (P=0.033). There was no observed significant difference in sleep quality score based on gender, age groups, number of active SIM cards, educational terms and level, residency place and marital status (Table 3).

Table 2. Comparison the sleep quality score in studied students based on social network of mobile
Social networks Not Used Used P value
Mean SD Mean SD
Viber4.98 2.185.542.430.026
WhatsApp 4.98 2.395.522.290.032
Tango5.12 2.266.052.560.002
Line5.07 2.295.742.390.008
Instagram5.23 2.385.512.480.323
Others5.33 2.335.192.430.672
Table 3. Comparison the sleep quality score in studied students based on demographic characteristics
Variables Sleep Quality Score P value
Mean SD
Gender 0.182
Female5.422.35
Male5.062.33
Educational level 0.170
Bachelor5.192.40
General physician5.372.20
Marital status 0.638
Single5.372.37
Married5.142.15
Residency 0.033
Parent’s house5.022.21
Dormitories5.562.49
Age group (yr) 0.923
<195.261.99
20-215.362.31
22-235.152.52
≥245.282.31
Number of SIM card 0.127
15.152.24
≥25.552.52
Semester 0.795
≤35.352.25
4-65.142.44
≥75.172.30

The mean score of sleep quality and different components of sleep quality is showed in Table 4. Sleep disturbances score was the poorest components of sleep quality and habitual sleep efficiency component was the best. However, the mean of all components of sleep quality and the whole score of sleep quality showed a direct significant correlation (r=0.369, P<0.001) by cell-phone addiction score. Nevertheless, the correlation of sleep duration score was negative by cell-phone addiction score. Moreover, there was a significant difference between normal and over-used cell-phone subjects regarding subjective sleep quality, sleep disturbances, use of sleeping medication, daytime dysfunction and total sleep quality. Nevertheless, this difference was not observed in sleep latency, sleep duration and habitual sleep efficiency.

Table 4. The sleep quality score and their different components in normal and over-used students
Components Overall Score Normal Over-used P value
Mean SD Mean SD Mean SD
Subjective sleep quality score0.990.750.930.711.500.880.001
Sleep latency Score1.050.811.040.791.170.910.310
Sleep duration score1.000.791.010.780.900.880.380
Habitual sleep efficiency score0.060.340.050.300.150.540.257
Sleep disturbances score1.700.531.030.521.330.530.002
Use of sleeping medication score0.160.510.130.480.330.700.025
Daytime dysfunction score0.970.890.900.871.480.910.001
Sleep quality Score5.302.355.102.246.902.600.001

Multivariate analysis (Table 5) showed that affected to cell-phone addiction, male gender and studying in general practitioner (GP) level are the most important predictors of poor sleep quality in medical students. So that, cell-phone addiction increased the probability of poor sleep quality more than 4.5 fold (OR=4.52, CI 95%: 1.8, 1.9). In addition, male students were at risk for 50% higher for poor sleep quality (OR=1.54, CI 95%: 1.08, 2.43) and studying in GP level was related with 61% increase in poor sleep quality (OR=1.61, CI 95%: 1.18, 2.54).

Table 5. The logistic regression results of poor sleep quality in medical students
Variables Normal sleep quality Poor sleep quality Odds ratio (95% CI) P value
Cell-phone addiction
No1341901.00
Yes5344.52 (1.80, 11.90)0.002
Gender
Female901611.00
Male49631.54 (1.08, 2.43)0.048
Educational level
Bachelor991371.00
General physician39861.61 (1.18, 2.54)0.040

Multivariate model adjusted based on the related covariates in univariate analysis including educational level, residency place, SIM card number and using social networks.


Discussion

According to results, male students studying in GP level are at risk of higher usage of mobile cell-phones and more addiction to cell-phones due to more contribution in social networks. Therefore, cell-phone addiction due to higher usage of social networks is effective on sleep quality and quantity in medical students. However, the relationship between over-use of cell-phones, internet addiction and sleep quality is reported 16,24-26 ,. University as one of the new places for students can cause sleep disturbance and decrease the sleep quality 6 . Another study in Turkish students proved the strong relationship between internet addiction and sleep 8 . Moreover, In Peru, suffering sleep disorders in people affected to Facebook addiction was 30% higher than normal people 7 . However, nowadays by increasing the touch and Android phones, the usage of web social networks increased via mobile cell-phones. Moreover, installing social networks applications on the android cell-phones enhanced the time wasting in the virtual webs and social networks. Therefore, internet addiction and cell-phone addiction are related due to both web and application’s social networks 8,18,27 .

According to univariate results, more usage from common social networks such as Viber, WatsApp, Line, and Tango observed in students with overused smartphones. Most of the students reported using these applications in the last hours of night or after midnight instead of having rest. These nightly engagements disturbance the sleep and wake cycle due to effect on melatonin spatter and increase the risk of mental health problems such as depression, stress, anxiety, and social dysfunction 10,16,28 . Melatonin is a hormone made by the Pineal gland; a small gland in the brain increased during the darkness and the highest natural levels of blood melatonin is in the night 29 . In addition, serotonin is responsible for happiness and freshness in day times 30 . Therefore, people who are busy with smartphone all the night and prefer to sleep in the daytime are affected to stress, anxiety and depression due to melatonin and serotonin deficiency and their brain and cognitive functions would be declined 9,29,31 . Since the brain can process information and prepare for actions during sleep by keeping information and delete those are not important 31 , lack of sleep can cause problems in brain process and receiving the information from environment. The current study results also showed the higher daily dysfunction in students affected to over-used cell-phone.

Male students and who studied in GP level were more addicted to smart cell-phones and suffered from poor sleep quality. In Iran, male students that used more chat rooms and resident in dormitories were more addicted to internet 14 . The same results acquired in current study. Moreover, half of studied subjects were students resident in university dormitories and deprived from family or relatives and entertainments with their friends such as picnic or party. Therefore, the student spend his/her time to be online in his smartphone instead of having useful social activity, useful entertainment, and educational activity. Unclear future is a depression risk factor for students 32 . However, lack of opportunity and facilities for social and entertaining activities are the reasons of using the smart cell-phone and social networks and this caused the poor sleep quality. It is clear that cheap and easy access of cell-phone entertainments help to increase the chance of sleep disorder in students. Today the researchers are focusing on sleep disorder that is one of the internet addiction complications via smartphones. Sleep disorder as one of the most important of internet addiction complication is related to headache, learning disability, memory diagnosis, aggressive behavior, and mental disorder and higher risk is cause of heart disease and diabetes 8,9,16,17,26,33 . However, future studies with multicenter design are suggested with higher sample size to assess the cultural and economic differences.


Conclusions

High usage of internet and social networks via smart cell-phones is related to poor sleep quality and quantity. Predefined sport, educational, cultural and interesting entertainment are the essential needs for all medical students especially in male students who are longer courses and are away of families to preventing cell-phone addiction and lower usage from social networks in smartphones.


Conflict of interest statement

The authors have no conflict of interest to declare.


References

  1. Sahin S, Ozdemir K, Unsal A, Temiz N. Evaluation of mobile phone addiction level and sleep quality in university students. Pak J Med Sci 2013; 29(4):913-918.
  2. The Official Portal of Measuring Information Society of Iran. ICT Facts‏ and Figures In Iran; ICT Development Index Status. 2014: available from:.
  3. http://www.itc.ir/portals/0/ICT_F&F_IRAN_1393_930605_Ver930601.930600.pdf‏.
  4. Wilson C, Boe B, Sala A, Puttaswamy KP, Zhao BY. User interactions in social networks and their implications. Proceedings of the 4th ACM European conference on Computer systems. April 1-3, 2009, Nuremberg, Germany‏.
  5. Kuss DJ, Griffiths MD. Online social networking and addiction—a review of the psychological literature. Int J Environ Res Public Health 2011; 8(9):3528-3552‏.
  6. Jafarkarimi H, Sim ATH, Saadatdoost R, Hee JM. Facebook Addiction among Malaysian Students. Int J Information Edu Technol 2016; 6(6):465-469‏.
  7. Cheng SH, Shih CC, Lee IH, Hou YW, Chen KC, Chen KT. A study on the sleep quality of incoming university students. ‏ Psychiatry Res 2012; 197(3):270-274‏.
  8. Wolniczak I1, Cáceres-DelAguila JA, Palma-Ardiles G, Arroyo KJ, Solís-Visscher R, Paredes-Yauri S. Association between Facebook dependence and poor sleep quality: a study in a sample of undergraduate students in Peru. PloS One 2013; 8(3):e59087‏.
  9. Canan F, Yildirim O, Sinani G, Ozturk O, Ustunel TY, Ataoglu A. Internet addiction and sleep disturbance symptoms among Turkish high school students. Sleep Biol Rhythms 2013; 11(3):210-213‏.
  10. Telzer EH, Fuligni AJ, Lieberman MD, Galván A. The effects of poor quality sleep on brain function and risk taking in adolescence. Neuroimage 2013; 71:275-283‏.
  11. Yogesh S, Abha S, Priyanka S. Short Communication Mobile usage and sleep patterns among medical students. Indian J Physiol Pharmacol 2014; 58(1):100-103.
  12. Shrivastava A, Saxena Y. Effect of mobile usage on serum melatonin levels among medical students. Indian J Physiol Pharmacol 2014; 58(4):395-399‏.
  13. Shaw M, Black DW. Internet addiction. CNS Drugs 2008; 22(5):353-365‏.
  14. Young KS. Internet addiction: The emergence of a new clinical disorder. Cyber Psychol Behav 1998; 1(3):237-244‏.
  15. Ghamari F, Mohammadbeigi A, Mohammadsalehi N, Hashiani AA. Internet addiction and modeling its risk factors in medical students, iran. Indian J Psychol Med 2011; 33(2):158-162.
  16. Tsimtsiou Z, Haidich AB, Spachos D, Kokkali S, Bamidis P, Dardavesis T. Internet addiction in Greek medical students: an online survey. Acad Psychiatry 2015; 39(3):300-304‏.
  17. Thomée S, Härenstam A, Hagberg M. Mobile phone use and‏ stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC Public Health 2011; 11(1):66‏.
  18. Kita T, Yoshioka E, Satoh H, Saijo Y, Kawaharada M, Okada E. Short sleep duration and poor sleep quality increase the risk of diabetes in Japanese workers with no family history of diabetes. Diabetes Care 2012; 35(2):313-318‏.
  19. Jenaro C, Flores N, Gómez-Vela M, González-Gil F, Caballo C. Problematic internet and cell-phone use: Psychological, behavioral, and health correlates. Addict Res Theory 2007; 15(3):309-320‏.
  20. Golmohammadian M, Yaseminejad P. Normalization, validity and reliability of cell-phone over-use scale (COS) among university students. J Soc Psycol 2011; 6(19):37-52.
  21. Atadokht A, Hamidifar V, Mohammadi I. Over-use and type of mobile phone users in high school students and its relationship with academic performance and achievement motivation. J School Psychol 2014; 3(2):122-136.
  22. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res 1989; 28(2):193-213‏.
  23. Farrahi J, Nakhaee N, Sheibani V, Garrusi B, Amirkafi A. Psychometric properties of the Persian version of the Pittsburgh Sleep Quality Index addendum for PTSD (PSQI-A). Sleep Breath 2009; 13(3):259-262‏.
  24. Moghaddam JF, Nakhaee N, Sheibani V, Garrusi B, Amirkafi A. Reliability and validity of the Persian version of the Pittsburgh Sleep Quality Index (PSQI-P). Sleep Breath 2012; 16(1):79-82‏.
  25. Sansone RA, Sansone LA. Cell phones: the psychosocial risks. Innov Clin Neurosci 2013; 10(1):33-37‏.
  26. Billieux J. Problematic use of the mobile phone: a literature review and a pathways model. Current Psychiat Rev 2012; 8(4):299-307‏.
  27. Billieux J, Philippot P, Schmid C, Maurage P, De Mol J, Van der Linden M. Is dysfunctional use of the mobile phone a behavioural addiction? confronting symptom‐based versus process‐based approaches. Clin Psychol Psychother 2015; 22(5):460-468.
  28. Adams SK, Kisler TS. Sleep quality as a mediator between technology-related sleep quality, depression, and anxiety. Cyberpsychol Behav Soc Netw 2013; 16(1):25-30‏.
  29. Cheung LM, Wong WS. The effects‏ of insomnia and internet addiction on depression in Hong Kong Chinese adolescents: an exploratory cross‐sectional analysis. J Sleep Res 2011; 20(2):311-317‏.
  30. Gandhi AV, Mosser EA, Oikonomou G, Prober DA. Melatonin is required for the circadian regulation of sleep. Neuron 2015; 85(6):1193-1199‏.
  31. Paula JR, Messias JP, Grutter AS, Bshary R, Soares MC. The role of serotonin in the modulation of cooperative behavior. Behav Ecol. 2016; In press.
  32. Breton J, Robertson EM. Memory Processing: The critical role of neuronal replay during sleep. Current Biol 2013; 23(18):R836-R838‏.
  33. Bayati A, Beigi M, Salehi M. Depression prevalence and related factors in Iranian students. Pak J Biol Sci 2009; 12(20):1371-1375‏.
  34. Barat Dastjerdi N, Sayadi S. Relationship between using social networks and internet addiction anddepression among students. J Res Behav Sci 2013; 10(5):332-341.
Submitted: 20 Jan 2016
Revised: 30 Mar 2016
First published online: 27 Mar 2016
EndNote EndNote

(Enw Format - Win & Mac)

BibTeX BibTeX

(Bib Format - Win & Mac)

Bookends Bookends

(Ris Format - Mac only)

EasyBib EasyBib

(Ris Format - Win & Mac)

Medlars Medlars

(Txt Format - Win & Mac)

Mendeley Web Mendeley Web
Mendeley Mendeley

(Ris Format - Win & Mac)

Papers Papers

(Ris Format - Win & Mac)

ProCite ProCite

(Ris Format - Win & Mac)

Reference Manager Reference Manager

(Ris Format - Win only)

Refworks Refworks

(Refworks Format - Win & Mac)

Zotero Zotero

(Ris Format - FireFox Plugin)

Abstract View: 351
PDF Download: 202
Full Text View: 259