INTRODUCTION

A smart phone is the mobile phone having advanced functions along with the basic features of making and receiving calls and text messages. These advanced functions include internet access and various ‘apps’. Significant increase in smart phone use and capabilities allow everyone to access internet, social media anytime anywhere. Smart phone is associated with use of internet and social media which are part and parcel of computed assisted technology.

Though there are lots of benefits of a smart phone but it may also lead to overuse and addiction which further have negative health consequences like sleep disturbances, circadian rhythm disturbances and mental health. Various theories have been proposed to explain the mechanism of sleep disturbances due to excessive use of electronic media like smart phones- interference with sleep through increased psychophysiological arousal, through bright light exposure to electromagnetic radiations and physical discomfort caused by prolonged media use1. Bright light may impact sleep in two ways: by delaying the circadian rhythm when exposure takes place in the evening2 and also by causing an immediate activation in itself.3,4 The authors have already found poor sleep quality among students with increasing hours of usage of mobile per day 5

The cell phones, tablets, computers and other electronic gadgets have become such a huge part of life that it’s often hard to put them down—even at bedtime. Whether it is surfing the web, playing a video game, or using your phone as an alarm clock in the late evening, it is being away from a restful night. The blue light emitted by screens on cell phones, computers, tablets, and televisions restrain the production of melatonin. Melatonin is a natural hormone made by our body's pineal gland. During the day the pineal gland is inactive. Melatonin secretion in humans exhibits diurnal variation: levels are lowest during the day, and peak overnight during sleep6. It is a hormone that controls your sleep/wake cycle or circadian rhythm7. Reducing melatonin makes it harder to fall and stay asleep. Melatonin exerts physiologic effects that are enhancement of immune response, scavenging of free radicals and suppression of tumour growth in humans and experimental animals 8. A decrease in nocturnal melatonin secretion in rodents chronically exposed to EMF has been reported 9.Experiments on humans acutely exposed to EMF for a night have not resulted in reproducible effects on serum melatonin or urinary excretion of its main metabolite, 6-sulfatoxymelatonin (6-OHMS) 10,11.

Numerous psychiatric problems related to excessive smartphone use have been identified, including depressive symptoms, anxiety 12and low self-esteem13.However, in a study of 755 university students, smartphone addiction was found to be significantly associated with depression, anxiety, obsessive–compulsive symptoms, and impulsivity14.

If smartphone use can cause mental illness, and to examine this, psychologist Dr. Jon Elhai and his colleagues conducted a systematic review of the literature of smartphone behaviors15. The researchers identified 23 peer-reviewed articles that investigated the relationship between problematic smartphone use and symptoms of anxiety and depression. The researchers found that smartphone use was in fact associated with symptoms of anxiety and depression, as well as increased experience of stress. The more participants used their smartphones, the more likely they were to experience symptoms associated with these disorders and report being stressed. Why might this relationship exist? It is possible that problematic smartphone use represents a form of addiction similar to internet addiction16. Problematic smartphone use - compulsive or excessive - could worsen symptoms of anxiety and depression. Research has found that some individuals experience intense anxiety when separated from their phones17, and some even exhibit withdrawal-like symptoms if they can't check their device18. 

Most people find it rewarding to check their apps and notifications. But some can become addicted to this positive feeling, compulsively checking their phones for updates. Such behaviour could become stressful, leading to worsening symptoms of anxiety and depression19. The "fear of missing out" (FOMO) could partially drive this cycle18, leading smartphone users to anxiously check their phones for fear of missing out on important social events or interactions. With smartphones so central to society, is mental health doomed because of technology? 

Before worrying too much, keep two important facts in mind. First, the researchers found that the effects greater smartphone use were weak to moderate, meaning that it does not guarantee worse mental health. Second, and perhaps more importantly, the studies examined were correlational, meaning that it is not clear if smartphone use causes symptoms of mental illnesses or if symptoms of mental illness cause greater smartphone use. For example, perhaps a depressed person is more likely to use their smartphone to seek out social interactions or evade their negative thoughts. Similarly, an anxious person might be driven to check their social media feeds by an anxiety such as FOMO18. And it might be that someone experiencing increased stress is more likely to check their smartphone as a distraction. These cases would lead to greater smartphone use without ever causing negative effects. Here the smartphone use may be a symptom rather than a cause.

We still have much to learn about problematic smartphone use and its relationship to anxiety and depression. It is unlikely that smartphone use causes any substantial spike in mental health problems for most people. However, it is worth keeping in mind that your symptoms of anxiety and depression might be related to how you use your phone. If you are concerned, discuss this issue with a trained mental health professional.

Medical professionals is having a great responsibility of the society to carve themselves as eminent doctors, so they are required to be alert & attentive during learning period . To acquire professional level knowledge & skills of patient care, medical students who already have lots of academic burden and sleep problems due to other factors, smartphone overuse/addiction if present may further complicate the things and may lead to significant stress which may be subsequently responsible for anxiety and depression.

The objective of this study was to evaluate whether

1. Usage of smart phone has effect on sleep time or sleep duration among the first year medical students.

2. Usage of smart phone has effect on serum melatonin levels

3. Academic usage and non-academic usage affects the academic performance

4. Effect of smart phone usage on the mental health i.e. anxiety and depression in young adult first year medical students

METHODS

This is a cross sectional study done on first year M.B.B.S students of both sexes in the age group 17-23 yrs. from urban and rural backgrounds of Government Medical College, Gondia during April – May 2017. The study was approved by Institutional Ethical committee. We planned for purposive sampling, so our sample size was 100 . 3 students were absent (47 boys and 50 girls). We grouped the students in two – first group comprises of low users ( using smart phone for less than or equal to 2hrs and They were informed about the purpose of the study and asked to participate in the study. An informed written consent was taken from all the volunteers medical students prior to the start of study. A brief questionaries’ was asked to all the study group regarding usage of mobile phone and sleep hours in night.

Blood samples were taken in morning and serum melatonin was estimated by ELISA kit ( Ebascience).

Hamilton’s anxiety and depression scale was applied to the study group. Hamilton’s anxiety scale was applied to the study group. Anxiety scale consists of 14 items, each defined by a series of symptoms, and measures both psychic anxiety (mental agitation and psychological distress) and somatic anxiety (physical complaints related to anxiety). Each item is scored on a scale of 0 (not present) to 4 (severe), with a total score range of 0–56, where <17 indicates mild severity, 18–24 mild to moderate severity and 25–30 moderate to severe 20.

Hamilton’s and depression scale was applied to the study group. This version contains 17 items (HDRS17) pertaining to symptoms of depression experienced . Method for scoring varies by version. For the HDRS17, a score of 0–7 is generally accepted to be within the normal range (or in clinical remission), while a score of 20 or higher (indicating at least moderate severity)21.

DATA ANALYSIS

Statistical analysis was done SPSS software version 21. Test used for data analysis and level of significance was Kruskal- Wallis test and Mann-Whitney U test

RESULTS

Table 1 Distribution of the smart phone usage
Smart phone usage No. Percentage
less than 2 hours 17 17.5%
2 to 5 hours 67 68%
5 to 7 hours 12 12.4%
more than 7 hours 1 1%

Table no. 1. Shows the distribution of smart phone usage in study population, with maximum numbers of students falling in the group of 2 to 5 hours usage.

Table 2 Correlation between sleep hours and smart phone use
Hours on smart phone No. Average Sleep duration in hours
_<2hrs low users 36 5.5+_0.87
>2 hrs high users 61 6.2+_1.17
p value 0.220 NS

Table no. 2. Shows the distribution of students with respect to smart phone usage and their average sleep duration. 61 students come under high users with average sleep duration of 5.5+_0.87 hours whereas 36 students come under low user group with the average sleep duration of 6.2+_ 1.17. From the above results we found that the sleep duration was long in high user groups as compared to low user group. The p value was 0.220 which is not significant.

Figure 1

Table 3 Correlation of bedtime with smart phone usage
Bed time No Smart phone usage p value
11PM 17 2.44+_1.30 0.051
11PM-12AM 33 2.67+_1.06
12-1AM 32 2.97+_1.51
1AM-2AM 15 3.77+_1.67

Table no. 3. Shows the correlation of bedtime with the smart phone usage. The above results show that the smart phone usage is more in the students whose bedtime is late. The p value was 0.051 which not statistically significant.

Table 4 Corr elation of smart phone usage with Melatonin level
Smart phone usage No. Melatonin levels p value
_<2hrs low users 36 32.76+_20.87 0.416
>2 hrs high users 61 29.58+_17.05

Table no.4. Correlation of smart phone usage with Melatonin level. We found lower levels of melatonin in high users as compared to low users. The p value was 0.416 which was statistically nonsignificant.

Table 5 Showing the distribution of smart phone usage ( academic and non-academic use)
Hours on smart phone No. Academic hours Nonacademic hours
_<2hrs low users 36 0.604+_0.447 1.11+_0.61
>2 hrs high users 61 1.26+_0.701 2.28+_1.27
p value 0.000* 0.000*
Table 6 Relation of smart phone use with Academic performance
Hours on smart phone No. Academic performance
_<2hrs low users 36 23.03+_2.95
>2 hrs high users 61 22.97+_3.38
p value 0.376

Table no. 5. shows the distribution of smart phone usage for academic purpose and non-academic purpose. There were 36 students whose smart phone usage was equal or less than 2hrs (low users) a day were using smart phone for lesser time for academic purpose as compared to the 61 students whose smart phone usage was more than 2hrs ( high users). the p value was found to be significant. The non-academic usage of the mobile was also lower in low users group of students as compared to the high users group of students. The p value was also found to be significant.

Table no.6 shows the academic performance in low users and high users. The academic performance was low in high users groups as compared to low user group. The p value was 0.376 which is statistically nonsignificant.

Table 7 Correlation of smart phone usage with Anxiety and Depression
Smart phone usage No. Anxiety score Depression score
_<2hrs low users 36 4.89+_4.9 3.94+_5.22
>2 hrs high users 61 6.04+_6.01 4.39+_5.51
p value Mann-Whitney U test 0.378 0.610

Table no.7. shows the correlation of smart phone usage with the anxiety and depression. Anxiety score was found to be higher in students with high users as compared to low users. The p value was 0.378 which is statistically nonsignificant. Similar results were found with depression score which was higher in high users as compared to low users. The p value was found to be 0.610 which was statistically nonsignificant.

DISCUSSION

The study results have shown 68% of students were having 2-5 hours of smart phone usage with majority of the study population falling in high user group (more than 2 hours).The smart phone usage was having an impact on sleep and caused sleep disturbances22. The smart phone usage was more in students whose bedtime was late, but the p value was not significant. Increased use of smart phone is associated with increased internet use, and increased Internet use is associated with shorter sleep duration22; later bedtimes and rise times22,23,24; longer sleep latencies24; and increased daytime tiredness in adolescents 22,23,. We also found that sleep duration was more in the high user students, though the p value was not significant. Our results were similar to the previous study 25.There are various theories regarding the effect of smartphone usage at bedtime on sleep quality. Some studies had shown that the use of smartphone affects sleeps through several mechanisms . One of them is sleep displacement; with the convenience of using a smartphone in bed, the time may fly without noticing and therefore displace the time of sleeping. According to previous studies, adolescents who owned smartphones were more likely to go to bed later, with the sleep duration not being affected26, which is almost similar to our finding with more sleep duration in high user groups. Another mechanism was that electromagnetic radiation emitted by mobile phones 30 minutes before sleeping was found to delay the onset of melatonin production which in turn might affect sleep 27. Evidences that EMR reduces melatonin in human being commenced with Wang (1989) who found that the workers who were exposed to radiation field had dose response increase in serotonin levels and indicated a dose response reduction in melatonin28. This study showed that non visible electromagnetic field exposure depresses the conversion of serotonin to melatonin. A study done by EL- Helaly revealed that electronic equipment repairers who were exposed to extremely low ELF field had a lower mean levels of serum melatonin than that of controls with a high statistically significant difference 29. A light-emitting diode (LED) emitted by smartphone screens was also reported to suppresses melatonin secretion which reduces sleepiness 30. We found low levels of melatonin in high user group, but the difference was not significant. The reduced melatonin level may be the result of disturbed circadian rhythm which may be responsible for sleeplessness and health effects 31.

A third proposed mechanism is emotional arousal. Smartphones serve as a way of communication between people and thus makes it a way of conducting bad or good news or even communicating a conflict between people which might increase emotional arousal at night, therefore, affects sleep. However, it has not yet been experimentally tested31. Incoming notifications, when the smartphone is not turned into silent mode, may wake up the individual and disrupt his/her sleep. Therefore we also tried to see the academic usage and the non-academic usage of the smart phone. As the more non academic usage will be having more emotional arousal. Our results shows that non-academic usage was higher in high user group as compared to low user group and the p value was statistically significant. So emotional arousal would be more in high users due high non-academic usage. This study which had focussed on the important confounding factor i.e. academic and non-academic use of smart phone. Academic usage was also found high in the high user group as compared to low user and the difference was statistically significant. This finding can be correlated to the academic performance in high user group, which is slightly low as compared to low user, but the difference is not statistically significant. High academic usage of smart phone may lead to better academic performance despite of late bedtime. Academic learning is now innovative as a result of smartphone and other media in promoting and advancing 21st century needed skills and knowledge 32. Students have experiences of digital surroundings in a tactile and personal manner, which is brought about by some mobile devices, including tablets and smartphones33. Mokoena et al argues that the use of smartphones by students improves collaborative learning through its connection to the internet34. This statement implies that the use of smartphones drives students to be more engaged in learner-centred participation learning. This is a vivid indication on numerous supports that smartphones have brought to the students; it advances their understanding by increasing academic performance, social media participation and information sharing; it helps their social skills by giving them opportunities to seek academic assistance and support, and many more 34(Mokoena, 2012). But some studies have also shown smart phone usage and application usage when studying has the negative effect on students’ academic performance35,36.

Numerous psychiatric problems related to excessive smartphone use have been identified, including depressive symptoms 37, anxiety37, and low self-esteem38. Smartphone addiction was found to be significantly associated with depression, anxiety, obsessive–compulsive symptoms, and impulsivity39. In the present study we found anxiety score and depression score was more in high user group as compared to low user, the difference was not statistically significant. Problematic smartphone use - compulsive or excessive - could worsen symptoms of anxiety (https://www.anxiety.org/what-is-anxiety) and depression. Research has found that some individuals experience intense anxiety when separated from their phones40, and some even exhibit withdrawal-like symptoms if they can't check their device41. Given these findings, it is possible that certain forms of smartphone use could cause or worsen symptoms of mental illness. problematic smartphone use represents a form of addiction similar to internet addiction42. Most people And it rewarding to check their apps and notifications. But some can become addicted to this positive feeling, compulsively checking their phones for updates. Such behaviour could become stressful, leading to worsening symptoms of anxiety and depression 43. The "fear of missing out" (FOMO) could partially drive this cycle41, leading smartphone users to anxiously check their phones for fear of missing out on important social events or interactions. With smartphones so central to society, is mental health doomed because of technology? Social media use are associated with risk for depression and anxiety44. The problematic smartphone use is responsible for poor sleep, late bedtime. We still have much to learn about problematic smartphone use and its relationship to anxiety and depression.

CONCLUSION

Smartphones affect the academic lives of the undergraduate medical students. In literature studies, there were arguments made for and against the impacts that smartphones have on the academic performance of students. This study can conclude with the fact that using smartphones made an impact on students both positively and negatively. Usage of smart phone has great impact on sleep, and is also associated with low melatonin levels. High Smart phone usage is correlated with anxiety and depression. But it is not clearly understood whether the smart usage is responsible for anxiety and depression or anxiety and depression causes high smart phone usage. It is unlikely that smartphone use causes any substantial spike in mental health problems for most people. However, it is worth keeping in mind that your symptoms of anxiety and depression might be related to how you use your phone. Other confounding factors need to be studied. Cell phones are undeniably convenient, helpful tools for study and can be a hurtful source of distraction depending on the attitude and use pattern of a student. Use of smart phone may be beneficial if use properly and cautiously can have better academic performance.1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45

References

  1. The assessment of anxiety states by rating Hamilton M. Br J Med Psychol.1959;32:50-55.
  2. Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users Cheever Nancy A., Rosen Larry D., Carrier L. Mark, Chavez Amber. Computers in Human Behavior.2014;37:290-297.
  3. Addictive Personality and Problematic Mobile Phone Use Takao Motoharu, Takahashi Susumu, Kitamura Masayoshi. CyberPsychology & Behavior.2009;12(5):501-507.
  4. Internet Addiction: A Systematic Review of Epidemiological Research for the Last Decade Kuss D., Griffiths M., Karila L., Billieux J.. Current Pharmaceutical Design.2014;20(25):4026-4052.
  5. Does evening exposure to mobile phone radiation affect subsequent melatonin production? Wood Andrew W., Loughran Sarah P., Stough Con. International Journal of Radiation Biology.2006;82(2):69-76.
  6. Mobile usage and sleep patterns among medical students Saxena Y, Shrivastava A, Singh P. IJPP.2014;58(1):103-106.
  7. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students Demirci Kadir, Akgönül Mehmet, Akpinar Abdullah. Journal of Behavioral Addictions.2015;4(2):85-92.
  8. Out of sight is not out of mind: The impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users Cheever Nancy A., Rosen Larry D., Carrier L. Mark, Chavez Amber. Computers in Human Behavior.2014;37:290-297.
  9. Effects of Electric and Magnetic Fields from High-power Lines on Female Urinary Excretion of 6-Sulfatoxymelatonin Levallois P.. American Journal of Epidemiology.2001;154(7):601-609.
  10. 5HT content changes in peripheral blood of workers exposed to microwave and high frequency radiation. Chung Hua Yu Fang I Wang S G. Hsueh Tsa Chih.1989;23(4):207-210.
  11. The Development and Maintenance of Drug Addiction Wise Roy A, Koob George F. Neuropsychopharmacology.2014;39(2):254-262.
  12. Mobile learning with Twitter to improve linguistic competence at secondary schools Cano E V. New Educational Review.2012;29(3):134-147.
  13. Impact of smartphone usage on students’ academic achievement: Roles of self-regulation and trust Rouis L, Limayem M, Salehi-Sangari E. Electronic Journal of Research in Educational Psychology.2011;9(3):961-994.
  14. The relationship between electromagnetic field and light exposures to melatonin and breast cancer risk: A review of the relevant literature Brainard George C., Kavet Robert, Kheifets Leeka I.. Journal of Pineal Research.1999;26(2):65-100.
  15. Associations between smartphone addiction proneness and psychopathology Lee S J, Kim B, Choi T K, Lee S H, Yook K H. Korean J of Biol Psychiatry.2014;21(4):161-167.
  16. The Association between Use of Mobile Phones after Lights Out and Sleep Disturbances among Japanese Adolescents: A Nationwide Cross-Sectional Survey Munezawa Takeshi, Kaneita Yoshitaka, Osaki Yoneatsu, Kanda Hideyuki, Minowa Masumi, Suzuki Kenji, Higuchi Susumu, Mori Junichiro, Yamamoto Ryuichiro, Ohida Takashi. Sleep.2011;34(8):1013-1020.
  17. Oxidative stress, melatonin level, and sleep insufficiency among electronic equipment repairers El-Helaly Mohamed, Abu-Hashem E. Indian Journal of Occupational and Environmental Medicine.2010;14(3):66-66.
  18. Sensitivity of the human circadian pacemaker to nocturnal light: melatonin phase resetting and suppression Zeitzer Jamie M., Dijk Derk-Jan, Kronauer Richard E., Brown Emery N., Czeisler Charles A.. The Journal of Physiology.2000;526(3):695-702.
  19. Internet Addiction: A Systematic Review of Epidemiological Research for the Last Decade Kuss D., Griffiths M., Karila L., Billieux J.. Current Pharmaceutical Design.2014;20(25):4026-4052.
  20. Sleep and use of electronic devices in adolescence: results from a large population-based study Hysing M., Pallesen S., Stormark K. M., Jakobsen R., Lundervold A. J., Sivertsen B.. BMJ Open.2015;5(1):e006748-e006748.
  21. A review of the evidence supporting melatonin’s role as an antioxidant Reiter R J, Melchiorri D, Sewwrynek E. J Pineal Res.1995;19:149-165.
  22. The Development and Maintenance of Drug Addiction Wise Roy A, Koob George F. Neuropsychopharmacology.2014;39(2):254-262.
  23. Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology Elhai Jon D., Dvorak Robert D., Levine Jason C., Hall Brian J.. Journal of Affective Disorders.2017;207:251-259.
  24. A Phase Response Curve to Single Bright Light Pulses in Human Subjects Khalsa Sat Bir S., Jewett Megan E., Cajochen Christian, Czeisler Charles A.. The Journal of Physiology.2003;549(3):945-952.
  25. ICT enabled classroom effectiveness scale development and validation: A case of multi-campus university Tikoria J, Agariya A K. Knowledge Management & E-Learning (KM&EL).2017;9(1):111-127.
  26. Adolescents’ Electronic Media Use at Night, Sleep Disturbance, and Depressive Symptoms in the Smartphone Age Lemola Sakari, Perkinson-Gloor Nadine, Brand Serge, Dewald-Kaufmann Julia F., Grob Alexander. Journal of Youth and Adolescence.2015;44(2):405-418.
  27. Smartphones and regular cellular phones: assessing their impact on students Mokoena S. 2012.
  28. Melatonin in Humans Brzezinski Amnon. New England Journal of Medicine.1997;336(3):186-195.
  29. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students Demirci Kadir, Akgönül Mehmet, Akpinar Abdullah. Journal of Behavioral Addictions.2015;4(2):85-92.
  30. Addictive Personality and Problematic Mobile Phone Use Takao Motoharu, Takahashi Susumu, Kitamura Masayoshi. CyberPsychology & Behavior.2009;12(5):501-507.
  31. The Extended iSelf: The Impact of iPhone Separation on Cognition, Emotion, and Physiology Clayton Russell B., Leshner Glenn, Almond Anthony. Journal of Computer-Mediated Communication.2015;20(2):119-135.
  32. A RATING SCALE FOR DEPRESSION Hamilton M.. Journal of Neurology, Neurosurgery & Psychiatry.1960;23(1):56-62.
  33. The Effect of Presleep Video-Game Playing on Adolescent Sleep Weaver Edward, Gradisar Michael, Dohnt Hayley, Lovato Nicole, Douglas Paul. Journal of Clinical Sleep Medicine.2010;06(02):184-189.
  34. MD, PhD Social Media Use and Depression and Anxiety Symptoms: A Cluster Analysis Shensa Ariel, Ma Jaime E, Sidani Mph Phd Mary Amanda, Dew Phd César G, Escobar-Viera Md Phd, Primack Brian A. Am J Health Behav.2018;42(2):116-128.
  35. Evening exposure to a light-emitting diodes (LED)-backlit computer screen affects circadian physiology and cognitive performance Cajochen C, Frey S, Anders D, Spati J, Bues M, Pross A. J Appl Physiol.1985;110:1432-1440.
  36. Mobile usage and sleep patterns among medical students Saxena Y, Shrivastava A, Singh P. Indian J Physiol Pharmacol.2014;58(1):100-103.
  37. Sleep and Television and Computer Habits of Swedish School-Age Children Garmy Pernilla, Nyberg Per, Jakobsson Ulf. The Journal of School Nursing.2012;28(6):469-476.
  38. Assessment of health effects from exposure to power line frequency electric and magnetic fields; working group report Portier C J, Wolfe M S. Research triangle Park NC 1998.
  39. Moyazzem Hossain Impact of Mobile Phone Usage on Academic Performance WSN Md 2019.
  40. Internet Addiction: A Systematic Review of Epidemiological Research for the Last Decade Kuss D., Griffiths M., Karila L., Billieux J.. Current Pharmaceutical Design.2014;20(25):4026-4052.
  41. Light treatment for sleep disorders: consensus report. III. Alerting and activating effects Campbell S S, Dijk D J, Boulos Z. J Biol Rhythms.1995;10.
  42. Sleep patterns, electronic media exposure and daytime sleep-related behaviours among Israeli adolescents Shochat Tamar, Flint-Bretler Ofra, Tzischinsky Orna. Acta Paediatrica.2010;99(9):1396-1400.
  43. The Extended iSelf: The Impact of iPhone Separation on Cognition, Emotion, and Physiology Clayton Russell B., Leshner Glenn, Almond Anthony. Journal of Computer-Mediated Communication.2015;20(2):119-135.
  44. Television viewing, computer game playing, and Internet use and self-reported time to bed and time out of bed in secondary-school children 11.Van den Bulck, J.. Sleep: Journal Of Sleep And Sleep Disorders Research.2004;27(1):101-104.
  45. Associations between smartphone addiction proneness and psychopathology Lee S J, Kim B, Choi T K, Lee S H, Yook K H. Korean J of Biol Psychiatry.2014;21(4):161-167.