Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Smartphones have become a part of our daily life, the number of people using smartphones is increasing day after day. Easy access to internet is the main advantage of smartphones in comparison to traditional mobile phones, so they are considered as handheld convenient substitutes to computers. People use smartphones for many different purposes such as communication, entertainment, browsing for information, education or business facilitation. Unfortunately the excessive use of smartphones makes people 'addicted' to that type of technology. Past research has shown that older people have less positive attitudes towards a variety of technologies and they are less likely than younger people to embrace new technology, so It seems that the problem of smartphones addiction is most likely to affect young people who are fascinated with new technologies.
What is smartphones addiction? The concept of smartphones addiction is not proposed yet for inclusion in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) as non-substance-related disorder but it can be considered one type of technological addictions which were operationally defined by Griffiths as non-chemical (behavioral) addictions which involve human-machine interaction and usually contain inducing and reinforcing features which may contribute to the promotion of addictive tendencies.
Furthermore, Recent work by Lin, et al identified the criteria for diagnosis smartphones addiction as following:
A. Behavioral criteria (3 or more should be present):
B. Functional impairment criteria (2 or more criteria should be present):
Factors associated with smartphones addiction:
Size of problem:
Prevalence of smartphones addiction in young people varies among countries as shown by studies: 29.6% in Saudi Arabia, 44.6% in Lebanon,16.9% in Switzerland, 21.3% in China and 31.33% in India.
-there is no available data about the size of this of this problem in Egypt so investigators need to conduct this study to determine the prevalence of smartphones addiction among young people and it adverse effect on different aspects.
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| University students | university students who use smartphones |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| self-administered structured questionnaire | Other | Data will be collected by self-administered structured questionnaire. The aim of the study and the way of filling the questionnaire will be explained to the students, and then he/she fills the questionnaire by him/her self. The questionnaire will assess smartphones addiction and some of its associated factors and its health consequences |
| Measure | Description | Time Frame |
|---|---|---|
| Smartphones addiction scale (SAS) | Prevalence of smartphones addiction and its adverse effects will be evaluated by a self-administered questionnaire guided by smartphones addiction scale developed by Kwon et al. | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Factors associated with smartphones addiction questionnaire | the study will assess some associated factors that may be risk factors to smartphones addiction as depression and loneliness | 6 months |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Egyptian Students of Assiut University will be the target population Target students will be selected randomly by using a multistage stratified cluster sampling technique.
At the first stage, Faculties within Assiut University will be stratified into 3 strata; Practical , Theoretical and Medical then faculties will be chosen randomly from each stratum to end up with 4 faculties (2 theoretical, 1 practical and 1 medical).
In the second stage, cluster sample will be chosen from one academic year within each faculty (practical sections or small classes). The clusters will be chosen through simple random sample.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Hosnia Said, MD | Contact | +201062170293 | hosniasa@yahoo.com | |
| Dalia Galal, MD | Contact | +201007120821 | daliaym2001@yahoo.com |
Not provided
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Assiut University | Asyut | 71111 | Egypt |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 25935253 | Result | Lin YH, Lin YC, Lee YH, Lin PH, Lin SH, Chang LR, Tseng HW, Yen LY, Yang CC, Kuo TB. Time distortion associated with smartphone addiction: Identifying smartphone addiction via a mobile application (App). J Psychiatr Res. 2015 Jun;65:139-45. doi: 10.1016/j.jpsychires.2015.04.003. Epub 2015 Apr 10. | |
| 24234164 | Result |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D001523 | Mental Disorders |
| D016739 | Behavior, Addictive |
| ID | Term |
|---|---|
| D003192 | Compulsive Behavior |
| D007175 | Impulsive Behavior |
| D001519 | Behavior |
Not provided
Not provided
Not provided
Not provided
Not provided
|
| Griffiths M. Gambling on the internet: A brief note. J Gambl Stud. 1996 Dec;12(4):471-3. doi: 10.1007/BF01539190. No abstract available. |
| 26132913 | Result | Demirci K, Akgonul M, Akpinar A. Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. J Behav Addict. 2015 Jun;4(2):85-92. doi: 10.1556/2006.4.2015.010. |
| 26551911 | Result | Kim SE, Kim JW, Jee YS. Relationship between smartphone addiction and physical activity in Chinese international students in Korea. J Behav Addict. 2015 Sep;4(3):200-5. doi: 10.1556/2006.4.2015.028. |
| 25931684 | Result | Kim HJ; DH; Kim JS. The relationship between smartphone use and subjective musculoskeletal symptoms and university students. J Phys Ther Sci. 2015 Mar;27(3):575-9. doi: 10.1589/jpts.27.575. Epub 2015 Mar 31. |
| 26690625 | Result | Haug S, Castro RP, Kwon M, Filler A, Kowatsch T, Schaub MP. Smartphone use and smartphone addiction among young people in Switzerland. J Behav Addict. 2015 Dec;4(4):299-307. doi: 10.1556/2006.4.2015.037. |
| 26672469 | Result | Nikhita CS, Jadhav PR, Ajinkya SA. Prevalence of Mobile Phone Dependence in Secondary School Adolescents. J Clin Diagn Res. 2015 Nov;9(11):VC06-VC09. doi: 10.7860/JCDR/2015/14396.6803. Epub 2015 Nov 1. |
| 27855666 | Result | Long J, Liu TQ, Liao YH, Qi C, He HY, Chen SB, Billieux J. Prevalence and correlates of problematic smartphone use in a large random sample of Chinese undergraduates. BMC Psychiatry. 2016 Nov 17;16(1):408. doi: 10.1186/s12888-016-1083-3. |
| 23468893 | Result | Kwon M, Lee JY, Won WY, Park JW, Min JA, Hahn C, Gu X, Choi JH, Kim DJ. Development and validation of a smartphone addiction scale (SAS). PLoS One. 2013;8(2):e56936. doi: 10.1371/journal.pone.0056936. Epub 2013 Feb 27. |