نشریه علمی پژوهشی طب انتظامی Journal of Police Medicine
Volume 11, Issue 1 (2022)
J Police Med 2022, 11(1) |
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Sharifi Rahnmo S, Seraji F, Sharifi Rahnmo M, Nouri E, Fathi A. Focal Analysis of the relation between the components of mobile addiction and the rate of young people high-risk driving behaviors. J Police Med 2022; 11 (1) : e6
URL: http://jpmed.ir/article-1-1023-en.html
URL: http://jpmed.ir/article-1-1023-en.html
1- Department of Curriculum Studies, Faculty of Humanities, Bu Ali Sina University, Hamadan, Iran
2- Department of Curriculum Studies, Faculty of Psychology and Educational Sciences, Allameh Tabatabaei University, Tehran, Iran
3- Department of Counseling, Faculty of Psychology and Educational Sciences, Mohaghegh Ardabili University, Ardabil, Iran
4- Research Institute of Law Enforcement Sciences & Social Studies, Tehran, Iran
2- Department of Curriculum Studies, Faculty of Psychology and Educational Sciences, Allameh Tabatabaei University, Tehran, Iran
3- Department of Counseling, Faculty of Psychology and Educational Sciences, Mohaghegh Ardabili University, Ardabil, Iran
4- Research Institute of Law Enforcement Sciences & Social Studies, Tehran, Iran
English Extended Abstract: (1633 Views)
Introduction
... [1-3]. The word “addiction” is used in recent studies to describe a wide range of behaviors, such as cell phone abuse [4]. Mobile addiction is a non-substance and behavioral addiction which is called as an obsessive-compulsive disorder [5]. [6-8]. One of the issues related to mobile addiction is high-risk driving [9]. ... [10-13]. According to UNICEF, accidents in Iran have increased significantly in recent years [14]. In 97.5% of accidents, the human factor the relative impact of which compared to other factors is 49% which implies a high impact, is directly involved. However, mobile addiction has a high level of significance among human factors [15]. Mobile addiction can be associated with risk-taking while driving because it ignores rules of conduct and norms that have formal enforcement guarantees.
Aim(s)
Due to the fact that various factors can play roles in mobile addiction and mobile addiction causes adverse effects on driving, the present study was conducted to identify the canonical relation of mobile addiction components with high-risk driving behaviors of young people.
Research Type
The present study is applied in terms of purpose and in terms of nature and method, is a descriptive correlation.
Research Society, Place and Time
The statistical population of this study was all students of Azad University with 15,000 students in Hamedan, Iran in 2021.
Sampling Method and Number
384 students were selected voluntarily using Krejcie and Morgan table.
Used Devices & Materials
The Standard Mobile Phone Addiction Questionnaire (Cronbach's alpha 0.87) and the Iranian Standard Risk Questionnaire (Cronbach's alpha 0.89) were used to collect data. ... [17, 18]. Due to the prevalence of Corona virus, research tools were designed on the Porsline website and its link was placed in cyberspace so that students whose driver's licenses’ dates of issue are for at least two years before could participate in the research.
Ethical Permissions
The ethics of the present study were fully observed.
Statistical Analysis
Correlation method was used to test the hypotheses. Data analysis was performed by observing the assumptions, using canonical correlation and multiple regression in SPSS 25 software; because canonical analysis has the ability to measure latent research variables that are not directly observable or measurable.
Finding by Text
No Attrition bias was found in the samples. Based on the data 47% of respondents (181) were male and 53% (203) were female. The mean age was 25.41±8.04. The mean of mobile addiction was 40.23 with a standard deviation of 11.38 and the mean of high-risk behaviors was 93.05 with a standard deviation of 20.84 (Table 1). According to the results of Kolmogorov-Smirnov test, the data were normal. There was a positive and direct relationship between the components of mobile addiction (inability to control desire, feeling anxious, deficiency and improving mood) and high-risk driving components (speeding, believing in control and violation of laws) at a significance level of less than 0.01. (Table 2). The correlation between canonical variables (predictor variables and criterion variable) was 0.381 and the square of the correlation was (0.132). The results showed that the common variance between these two sets (linear composition) was significant at the level of 0.000. In other words, 12.5% of the changes in the canonical variable of high-risk driving could be predicted by knowing the canonical variable of mobile addiction. Also, the canonical functions of two and three were not statistically significant at the significant level (Table 3). The results showed that the component of feeling anxious and deficient in the first set had the highest standard coefficients related to the mobile addiction variable. In fact, with an increase of one unit in the component of feeling anxious and lacking, the canonical correlation increased by 65.8%. Also in the criterion variable, with a single unit increase in the tendency to speed, the correlation increased by 71.7% (Table 4). Canonical scores showed that in the formation of predictor variables, the highest share was related to feelings of anxiety and deficiency (0.928) and in the criterion variable, the highest share was related to the tendency to speed (0.893).
Main comparison to the similar studies
There was a direct and positive relationship between the components of mobile addiction and high-risk driving behaviors of young people, which is in line with the previous research of many researchers [8, 19-23]. For example, Amit and Neha have shown that mobile addiction has a positive and significant relationship with high-risk behaviors, social anxiety, depression and loneliness. They have reported more social anxiety of male teenagers when using addictive mobile games, which can have result in similar damage while driving [23]. People who use mobile phones and related features such as the Internet more than others may replace the stronger relationships they have had in real life with lower quality social relationships, so the result is that they are more likely to engage in risky activities such as high-risk driving. In this regard, it can be said that perhaps the mobile phone provides an alternative to a sedentary life of people that may cause feelings of loneliness and isolation to spend more time using the Internet or people who use the Internet a lot, their social relationships are reduced. This issue increases the use of mobile phones and also increases tensions in people. In fact, the use of mobile, the Internet and virtual networks creates relationships for people and puts users at risk by reducing their individual sensitivity. Therefore, it can be said that a person who is addicted to mobile phones will feel compelled to check it in different situations. These situations may occur while driving and in places where the use of mobile phones is prohibited, which is a kind of law violation. To draw others’ attention, individual may also engage in cyber-behaviors that are contrary to the custom of society; such as abnormalities while driving, which may lead to prosecution; [24].
Limitations
One of the limitations of this research is the social and cultural characteristics of society and considerations that people in many cases, especially in areas such as high-risk behaviors, be conservative and want to look good. This is especially evident in the present study, which on the one hand uses self-report scales and on the other hand has been implemented in an environment such as a university. Also, face-to-face access to the samples was not possible due to its extent and Covid-19.
Suggestions
It is recommended that when issuing a driver's license, in addition to examining physical and mental health, the dimensions of mental and emotional addiction be also examined; also, interventions aimed at increasing the ability to regulate emotion may be helpful in reducing high-risk driving style. Taking personality tests can be helpful when obtaining or renewing the driver's license.
Conclusions
When a person is addicted to mobile, he has a constant feeling of controlling it in different situations. These situations may occur while driving and in places where the use of mobile phones is prohibited, which will lead to some kind of law violation and harmful consequences.
Clinical & Practical Tips in Police Medicine
The present study has tried to explain and use the results of the canonical relationships of mobile addiction with the rate of high-risk driving behaviors of young people, reduce such an outcome in society and provide effective assistance to the police. Because reducing such cases is associated with increasing the social health of young people and also reduces high-risk driving behaviors, and as a result, increases public order and the sense of social security in society.
Acknowledgments
We would like to thank all the participants in the research and all the loved ones who helped us in this research.
Conflict of interest
The authors state that there is no conflict of interest in the present study.
Funding Sources
The present study had no financial support.
Table 2) Correlation coefficient test of the relationship between
mobile addiction components and high-risk driving
Table 4) Standard coefficients and canonical scores of components
in the functions of criterion and predictor variables
... [1-3]. The word “addiction” is used in recent studies to describe a wide range of behaviors, such as cell phone abuse [4]. Mobile addiction is a non-substance and behavioral addiction which is called as an obsessive-compulsive disorder [5]. [6-8]. One of the issues related to mobile addiction is high-risk driving [9]. ... [10-13]. According to UNICEF, accidents in Iran have increased significantly in recent years [14]. In 97.5% of accidents, the human factor the relative impact of which compared to other factors is 49% which implies a high impact, is directly involved. However, mobile addiction has a high level of significance among human factors [15]. Mobile addiction can be associated with risk-taking while driving because it ignores rules of conduct and norms that have formal enforcement guarantees.
Aim(s)
Due to the fact that various factors can play roles in mobile addiction and mobile addiction causes adverse effects on driving, the present study was conducted to identify the canonical relation of mobile addiction components with high-risk driving behaviors of young people.
Research Type
The present study is applied in terms of purpose and in terms of nature and method, is a descriptive correlation.
Research Society, Place and Time
The statistical population of this study was all students of Azad University with 15,000 students in Hamedan, Iran in 2021.
Sampling Method and Number
384 students were selected voluntarily using Krejcie and Morgan table.
Used Devices & Materials
The Standard Mobile Phone Addiction Questionnaire (Cronbach's alpha 0.87) and the Iranian Standard Risk Questionnaire (Cronbach's alpha 0.89) were used to collect data. ... [17, 18]. Due to the prevalence of Corona virus, research tools were designed on the Porsline website and its link was placed in cyberspace so that students whose driver's licenses’ dates of issue are for at least two years before could participate in the research.
Ethical Permissions
The ethics of the present study were fully observed.
Statistical Analysis
Correlation method was used to test the hypotheses. Data analysis was performed by observing the assumptions, using canonical correlation and multiple regression in SPSS 25 software; because canonical analysis has the ability to measure latent research variables that are not directly observable or measurable.
Finding by Text
No Attrition bias was found in the samples. Based on the data 47% of respondents (181) were male and 53% (203) were female. The mean age was 25.41±8.04. The mean of mobile addiction was 40.23 with a standard deviation of 11.38 and the mean of high-risk behaviors was 93.05 with a standard deviation of 20.84 (Table 1). According to the results of Kolmogorov-Smirnov test, the data were normal. There was a positive and direct relationship between the components of mobile addiction (inability to control desire, feeling anxious, deficiency and improving mood) and high-risk driving components (speeding, believing in control and violation of laws) at a significance level of less than 0.01. (Table 2). The correlation between canonical variables (predictor variables and criterion variable) was 0.381 and the square of the correlation was (0.132). The results showed that the common variance between these two sets (linear composition) was significant at the level of 0.000. In other words, 12.5% of the changes in the canonical variable of high-risk driving could be predicted by knowing the canonical variable of mobile addiction. Also, the canonical functions of two and three were not statistically significant at the significant level (Table 3). The results showed that the component of feeling anxious and deficient in the first set had the highest standard coefficients related to the mobile addiction variable. In fact, with an increase of one unit in the component of feeling anxious and lacking, the canonical correlation increased by 65.8%. Also in the criterion variable, with a single unit increase in the tendency to speed, the correlation increased by 71.7% (Table 4). Canonical scores showed that in the formation of predictor variables, the highest share was related to feelings of anxiety and deficiency (0.928) and in the criterion variable, the highest share was related to the tendency to speed (0.893).
Main comparison to the similar studies
There was a direct and positive relationship between the components of mobile addiction and high-risk driving behaviors of young people, which is in line with the previous research of many researchers [8, 19-23]. For example, Amit and Neha have shown that mobile addiction has a positive and significant relationship with high-risk behaviors, social anxiety, depression and loneliness. They have reported more social anxiety of male teenagers when using addictive mobile games, which can have result in similar damage while driving [23]. People who use mobile phones and related features such as the Internet more than others may replace the stronger relationships they have had in real life with lower quality social relationships, so the result is that they are more likely to engage in risky activities such as high-risk driving. In this regard, it can be said that perhaps the mobile phone provides an alternative to a sedentary life of people that may cause feelings of loneliness and isolation to spend more time using the Internet or people who use the Internet a lot, their social relationships are reduced. This issue increases the use of mobile phones and also increases tensions in people. In fact, the use of mobile, the Internet and virtual networks creates relationships for people and puts users at risk by reducing their individual sensitivity. Therefore, it can be said that a person who is addicted to mobile phones will feel compelled to check it in different situations. These situations may occur while driving and in places where the use of mobile phones is prohibited, which is a kind of law violation. To draw others’ attention, individual may also engage in cyber-behaviors that are contrary to the custom of society; such as abnormalities while driving, which may lead to prosecution; [24].
Limitations
One of the limitations of this research is the social and cultural characteristics of society and considerations that people in many cases, especially in areas such as high-risk behaviors, be conservative and want to look good. This is especially evident in the present study, which on the one hand uses self-report scales and on the other hand has been implemented in an environment such as a university. Also, face-to-face access to the samples was not possible due to its extent and Covid-19.
Suggestions
It is recommended that when issuing a driver's license, in addition to examining physical and mental health, the dimensions of mental and emotional addiction be also examined; also, interventions aimed at increasing the ability to regulate emotion may be helpful in reducing high-risk driving style. Taking personality tests can be helpful when obtaining or renewing the driver's license.
Conclusions
When a person is addicted to mobile, he has a constant feeling of controlling it in different situations. These situations may occur while driving and in places where the use of mobile phones is prohibited, which will lead to some kind of law violation and harmful consequences.
Clinical & Practical Tips in Police Medicine
The present study has tried to explain and use the results of the canonical relationships of mobile addiction with the rate of high-risk driving behaviors of young people, reduce such an outcome in society and provide effective assistance to the police. Because reducing such cases is associated with increasing the social health of young people and also reduces high-risk driving behaviors, and as a result, increases public order and the sense of social security in society.
Acknowledgments
We would like to thank all the participants in the research and all the loved ones who helped us in this research.
Conflict of interest
The authors state that there is no conflict of interest in the present study.
Funding Sources
The present study had no financial support.
Table 1) Descriptive statistics on mobile addiction and high-risk driving
| Variable | Minimum | Maximum | Mean | Standard Deviation |
| Mobile addiction | 16 | 66 | 40.23 | 11.38 |
| High-risk behaviors | 18 | 156 | 93.05 | 20.84 |
Table 2) Correlation coefficient test of the relationship between
mobile addiction components and high-risk driving
| Variable | Inability to control desire | Feeling anxious and deficiency | Improve mood |
| Tendency to speed | 0.241 | 0.320 | 0.184 |
| Believe in control | 0.198 | 0.198 | 0.214 |
| Violation of laws | 0.167 | 0.110 | 0.172 |
p<0.01
Table 3) Results of canonical correlation analysis of the relationship between
the set of mobile addiction components and the set of high-risk driving
Table 3) Results of canonical correlation analysis of the relationship between
the set of mobile addiction components and the set of high-risk driving
| Canonical Functions | Special Amount | Canonical Correlation | The Square of Correlation | Lambda Wilkes | F | DF1 | p |
| 1 | 0.143 | 0.381 | 0.132 | 0.862 | 6.625 | 9 | 0.000 |
| 2 | 0.015 | 0.122 | 0.014 | 0.985 | 1.478 | 4 | 0.207 |
| 3 | 0.000 | 0.004 | 0.000016 | 1.00 | 0.008 | 1 | 0.929 |
Table 4) Standard coefficients and canonical scores of components
in the functions of criterion and predictor variables
| Canonical Functions | Function Components | Standard Coefficients | Canonical Scores |
| Predictive variable | Inability to control desire | 0.319 | 0.705 |
| Feeling anxious and deficiency | 0.658 | 0.928 | |
| Improve mood | 0.252 | 0.652 | |
| Criterion variables | Tendency to speed | 0.717 | 0.893 |
| Believe in control | 0.288 | 0.661 | |
| Violation of laws | 0.287 | 0.591 |
Article number: e6
Article Type: Original Research |
Subject:
Traffic Medicine
Received: 2021/05/20 | Accepted: 2021/10/19 | Published: 2021/12/31
Received: 2021/05/20 | Accepted: 2021/10/19 | Published: 2021/12/31
Extended Abstract [HTM 29 KB] (61 Download)
References
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4. Chóliz M. Mobile- phone addiction in adolescence: the test of mobile phone dependence (TMD). Prog Health Sci. 2012;2(1):33-44. http://cejsh.icm.edu.pl/cejsh/element/bwmeta1.element.ceon.element-72e09d7b-c90e-39bb-8a2c-1979dde32ef6
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11. Moghanizadeh Z, Sanagouy G, Talebi Z, Asvadi M. The Internet addiction: How university students are affected? J Edu Strateg Med Sci. 2017;11(5):44-52. [Persian]. DOI: 10.29252/edcbmj.11.05.05
12. Dadras Z, Faramariani S, Jafari A and Biabani G. Addiction to mobile social networking and its cultural and behavioral effects (Case study: female high school students). New Med Stud. 2019;5(19):117-50. [Persian]. https://dx.doi.org/10.22054/nms.2020.26685.346.
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19. Ishii K. Examining the adverse effects of mobile phone use among Japanese adolescent. Keio Commun Rev. 2011;33:69-83. https://www.semanticscholar.org/paper/Examining-the-AdverseEffects-of-MobilePhoneUseIshii/34b1cb3eebc380fa248906c8d6afb897927101d1#citing-papers.
20. Billieux J, Maurage P, Lopez-Fernandez O, Kuss D, Griffiths M. Can disordered mobile phone use be considered a behavioral addiction? An update on current evidence and a comprehensive model for future research. Curr Addict Rep. 2015;2:156-62. doi:10.1007/s40429-015-0054-y. [DOI:10.1007/s40429-015-0054-y]
21. Choliz M. Mobile phone addiction: a point of issue. Addiction. 2010;105(2):373-4. DOI: 10.1111/j.1360-0443.2009.02854.x [DOI:10.1111/j.1360-0443.2009.02854.x]
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25. Janabadi H, Shirani A. On the relationship between loneliness and social support with cell phone addiction among students. J School Psychol. 2017;5(4):7-30.[Persian]. http://jsp.uma.ac.ir/?_action=article&au=12952&_au=h++j&lang=en.
26. Rimmö P. Aberrant driving behaviour: homogeneity of a four-factor structure in samples differing in age and gender. Ergonomics. 2002;45(8):569-82. [DOI:10.1080/00140130210145873] [PMID]
27. Heidari M, Hashemi T. Emotional regulation and behavioral brain systems in the occurrence of high-risk driving behaviors. Rahvar Sci Q.(2017);6(23):151-74. [Persian]. http://talar.jrl.police.ir/article_18979_26b520eb806cf6d58e63cb2af1784e9b.pdf
28. Chóliz M. Mobile- phone addiction in adolescence: the test of mobile phone dependence (TMD). Prog Health Sci. 2012;2(1):33-44. http://cejsh.icm.edu.pl/cejsh/element/bwmeta1.element.ceon.element-72e09d7b-c90e-39bb-8a2c-1979dde32ef6
29. Ashayeri T, Mohammadi M, Namian F, Amin Fallah Z. Sociological survey of drivers' violations and factors affecting it. Rahvar Sci. (2020);9(33):121-44. [Persian]. http://talar.jrl.police.ir/article_94707.html
30. Askari A, Pandi H, Fonoudi M. The relationship between mental health and driving behaviors in tehran: The mediating role of mindfulness. Rahvar Sci Q. 2019;8(28):55-86. [Persian]. https://www.sid.ir/en/journal/ViewPaper.aspx?ID=724983
31. Afsari M, Hashemi S , Moghisi A. Predicting driving behavior based on emotional intelligence and perception of driving risk. Rahvar Sci Q. 2020;9(33):53-80. [Persian]. https://www.magiran.com/paper/2184410/?lang=en
32. Yasminejad P, GolMohammadian M, Yousefi N. The relationship between public health and excessive cell phone use in students. Knowl Res Appl Psychol. 2012;13(1):60-72. [Persian]. https://www.sid.ir/fa/journal/ViewPaper.aspx?id=164655
33. Mazaheri M, Karbasi M, Ehteshami M. The use of mobile phone features among students in Isfahan university of medical sciences. Health Syst Res 2014;10(2);276-85. [Persian]. https://www.sid.ir/EN/JOURNAL/ViewPaper.aspx?ID=429386 [DOI:10.5812/scimetr.18760]
34. ZadehMohammadi A. Ahmadabadi Z, Heidari M. Construction and assessment of psychometric features of Iranian adolescents risk taking scale. J Psychiatr. 2011;17(3):218-25. [Persian]. http://ijpcp.iums.ac.ir/article-1-1417-en.html
35. Moghanizadeh Z, Sanagouy G, Talebi Z, Asvadi M. The Internet addiction: How university students are affected? J Edu Strateg Med Sci. 2017;11(5):44-52. [Persian]. DOI: 10.29252/edcbmj.11.05.05
36. Dadras Z, Faramariani S, Jafari A and Biabani G. Addiction to mobile social networking and its cultural and behavioral effects (Case study: female high school students). New Med Stud. 2019;5(19):117-50. [Persian]. https://dx.doi.org/10.22054/nms.2020.26685.346.
37. Pourshafei H, Naderi F. The status of internet addiction and its predictive role in students' social health;(Case study: Birjand university). Khorasan Cult-Soc Stud Q. 2018;12(4):33-56. [Persian]. https://www.farhangekhorasan.ir/article_87671.html
38. Tian Y, Yu Ch, Lin Sh, Lu J, Liu Y, Zhang W. Sensation seeking, deviant peer affiliation, and internet gaming Addiction Among Chinese Adolescents: The Moderating Effect of parental knowledge. Front Psychol. 2019;9:1-7. doi: 10.3389/fpsyg.2018.02727. [DOI:10.3389/fpsyg.2018.02727] [PMID] [PMCID]
39. Wang J, Sheng J, Wang H. The association between mobile game addiction and depression, social anxiety and loneliness. Front Pub Health. 2019;7:1-6. [DOI:10.3389/fpubh.2019.00247] [PMID] [PMCID]
40. Amit K, Neha G. Mobile addiction and mental health of college students. Edu Quest- Int J Edu Appl Soc Sci. 2016;7(2):87-90. DOI:10.5958/2230-7311.2016.00023.4. [DOI:10.5958/2230-7311.2016.00023.4]
41. fathi A. The role of mental health components in high-risk driving behaviors . J Police Med. 2020;9(3):143-8. [Persian]. http://dx.doi.org/10.30505/9.3.143
42. Shahbazian A, Husaynpourbanadig K, Rahnemayibastam A. Survey the role of moral intelligence and cell phone addiction in academic procrastination of students. J Educ Strateg Med Sci. 2019;11(5):76-83. [Persian]. DOI: 10.29252/edcbmj.11.05.09.
43. Ishii K. Examining the adverse effects of mobile phone use among Japanese adolescent. Keio Commun Rev. 2011;33:69-83. https://www.semanticscholar.org/paper/Examining-the-AdverseEffects-of-MobilePhoneUseIshii/34b1cb3eebc380fa248906c8d6afb897927101d1#citing-papers.
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