Medical Students' Internet Addiction And Its Impact On Academic Achievement

doi:10.21203/rs.3.rs-4117958/v1

Download PDF

Research Article

Medical Students' Internet Addiction And Its Impact On Academic Achievement

https://doi.org/10.21203/rs.3.rs-4117958/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction

Addiction is a chronic, recurrent, relapsing disorder characterized by the compulsive pursuit of certain substances or certain behaviors, and the continuation of them despite the harmful consequences resulting from them and the long-term changes that occur in the brain. Excessive computer use meets the criteria for addiction. There are two types of addiction, physical addiction and non-physical addiction, such as Internet addiction.

Methods and Materials

A pivotal cross-sectional study, in which data were collected on a simple, random basis through an electronic questionnaire with three sections that was distributed to medical students at the Syrian Private University. 219 students were accepted into the sample (n = 219).

Results

The results presented that there is a significant correlation between Internet addiction and the percentile in the last academic year (P = .015), and there is a significant correlation between Internet addiction and the presence of previous traumas (P = .016) and the presence of severe family problems (P = .000).

Discussion

The results of our study were consistent with most of the results of previous studies, except that our study did not present any association of Internet addiction with gender, which has been proven to exist in other studies.

Conclusion

As evidenced by this brief study, the field of Internet addiction is advancing rapidly even without formal recognition of it as a separate and distinct behavioral addiction and with continuing disagreement over diagnostic criteria. The debate over whether IAD should be classified as a (behavioral) addiction, an impulse control disorder, or even obsessive-compulsive disorder cannot be satisfactorily resolved. But the symptoms we have observed in clinical practice present a great deal of overlap with symptoms typically associated with (behavioral) addiction.

Psychiatry

Internet Addiction

Medical Students'

The definition of addiction is "the fact or condition of being addicted to a particular substance, thing, or activity." Medically, it is a "chronic, recurrent, relapsing disorder characterized by a compulsive pursuit of certain substances or certain behaviors, and the continuation of them despite the harmful consequences resulting from them and the long-term changes that occur in the brain". There are two main concepts of addiction which include physical addiction (drug addiction), and non-physical addictions (behavioral addictions).

There is complex interplay between neurobiology, genetics, and environment—that is, natural and acquired factors—that play a role in the development of addiction, alcohol, and other substance use disorders. The five stages of addiction (First use, Continued use, Endurance, Accreditation, Addiction). These stages of addiction apply to either physical addiction or non-physical addiction.

Excessive computer use meets the criteria for addiction. Internet addiction (misuse) (IAD) has been studied extensively and is already currently under consideration for inclusion in the DSM-V. Some researchers and mental health practitioners consider excessive Internet use as a symptom of another disorder such as anxiety or depression rather than a separate entity. Internet addiction can also be considered an impulse control disorder (not otherwise specified). However, there is a growing consensus that this set of symptoms is addictive.

All addictions, whether chemical or behavioral, share certain characteristics including salience, compulsive use (loss of control), mood modification, relief of distress, tolerance, withdrawal, and persistence despite negative consequences. Types of Internet Addiction are Net Compulsions, Information Seeking, Gaming, Cybersex, Cyber Relationships.

Researchers have presented that younger populations, especially college students, are at greater risk of addiction due to their psychological, social, and environmental characteristics. Many factors have been attributed to this weakness, such as unstructured time, unlimited access to the Internet, a greater desire to use the latest tools and technologies, with less parental control, as well as the desire to develop a sense of identity and develop a close and meaningful relationship.

Aimes

This research aims were studying spread of Internet addiction among medical students at the Syrian Private University; the impact of Internet addiction on students’ academic achievement; studying statistically significant correlations between variables.

Study Design

Type of study: Observational study in the form of a cross-sectional study.

Target group: Clinical years students (4th, 5th, and 6th years) at the Faculty of Human Medicine at the Syrian Private University (SPU) in Syria.

Sampling: The sample selection was simple and random.

Data were collected in the form of electronic questionnaires designed for this research purpose, based on the adapted Young's test. These questionnaires were published on university students' social networking sites. This takes place at 2020.

Data collection tool

The data collection tool in the research was an electronic questionnaire. It was published on the social networking sites of clinical years students at the Faculty of Human Medicine at the Syrian Private University. The published questionnaire included 3 sections of questions.

The first section of the questionnaire contained questions related to demographic information. Among the personal information that was asked about were: age, gender, year of study, smoking, and percentage rate for the last year of study. As for the second section of the questionnaire, it included questions about the impact of the Internet on study and social life. In the third section questions were included, which included a survey about the most popular sites used on the Internet, the number of hours of daily Internet use, and the number of daily study hours.

Approvals:

Ethical approval was obtained from the Deanship of the Faculty of Human Medicine at the Syrian Private University to conduct this research and collect data from students. Informed consent was also obtained from the students by informing them of their participation in this research in the first part of the electronic questionnaire that was published.

Statistical Analyses

The data were entered into the Statistical Package for the Social Sciences (SPSS V.22), where all variables were coded and both descriptive and inferential statistical methods were followed to analyze the data and answer the research hypotheses. Descriptive statistics were used to extract percentages, standard deviation, and most frequent values in the sample. The internal consistency test of the questionnaire (Cronbach Alpha test) was used to study the internal consistency between the research elements and variables. The Shapiro-Wilk test for normal distribution was also used to study the pattern of data distribution and thus determine the appropriate statistical tests for the type of data, whether parametric or non-parametric tests.

Internal consistency test for the questionnaire:

Table 1 presents the result of the Cronbach Alpha test, where the result indicates a value equal to (0.735), which indicates good internal consistency and that the questionnaire is applicable and used to collect data and answer research hypotheses, (acceptable if test result value exceeds (0.60)).

Table 1

Internal consistency test (Cronbach’s alpha) for the questionnaire
		Frequency	Percentage	Test result	Number of elements
Elements	Included	219	100.0	0.735	22
	Excluded	0	.0
	Total	219	100.0

Testing the normal distribution of data:

Table 2 presents the result of the Shaprio-Wilk test. This test is used to determine the pattern of data distribution, whether normally or non-normally distributed, whether for quantitative or significant data. It appears from the table that the test result for all studied variables was less than (0.05), which indicates an abnormal distribution of the data, and therefore nonparametric tests must be used to answer the research hypotheses.

Table 2

Test of normal distribution of data
Variables	Shapiro-Wilk
Variables	value	df	Significance level
Age	.843	219	.000
Gender	.629	219	.000
Academical year	.776	219	.000
smoking	.592	219	.000
Academic average percentage	.714	219	.000
Father's education	.582	219	.000
Mother's education	.697	219	.000
Number of siblings	.740	219	.000
Current residence	.430	219	.000
Place of birth	.629	219	.000
Are there severe family problems?	.407	219	.000
Are there previous severe traumas in life??	.632	219	.000
Study time wasted because of the Internet?	.664	219	.000
Has excessive Internet use affected the grades?	.742	219	.000

Table 2 - Test of normal distribution of data

Variables	Shapiro-Wilk
Variables	value	df	Significance level
Do you find more attention online than you get in real life??	.801	219	.000
Do you find it difficult to concentrate while studying and escape to the virtual world?	.777	219	.000
Have you faced family problems due to spending a long time on the Internet and neglecting your studies?	.809	219	.000
Have you tried to control your Internet use and failed?	.809	219	.000
Do you miss lectures because you are busy on the Internet?	.368	219	.000
Can you study for two hours continuously without wanting to open the Internet?	.708	219	.000
Did your academic grades decline due to excessive use of the Internet?	.805	219	.000
Do you find it difficult to concentrate with the doctor during the lecture because of the intense desire to access the Internet??	.673	219	.000
Does the Internet cause you to stay up late??	.765	219	.000
Does your preoccupation with the Internet prevent you from spending enough time with your family??	.787	219	.000
Do you feel regret after spending a long time online??	.789	219	.000
What is the social networking site you spend the most time on:	.661	219	.000
How much social media sites take up your time?	.733	219	.000
How much time does studying take from your day?	.698	219	.000

Demographic variables of the sample (1st section of questionnaire)

Table 3 presents the descriptive analysis of the demographic variables, which include (age, gender, year of study, smoking, father’s and mother’s education, percentage, number of siblings, place of residence, place of birth, presence of family problems, and presence of previous psychological traumas).

Table 3

Descriptive analysis of demographic variables for the sample
		Frequency	Percentage	Mean	Minimum value	Supreme value	standard deviation
Age		219		23.36	22.00	28.00	1.25
Gender	Female	125	57.1%
Gender	Male	94	42.9%
Academical Year	4th year	50	22.8%
	5th year	71	32.4%
	6th year	98	44.7%
Smoking	Yes	72	32.9%
Smoking	No	147	67.1%
Academic average percentage		219		78.84	62.00	94.00	9.93
Father’s education	University	162	74.0%
	High school	40	18.3%
	Preparatory	17	7.8%
Mother’s education	University	133	60.7%
	High school	59	26.9%
	Preparatory	27	12.3%
Number of siblings		219		3.42	.00	23.00	2.31
Residential place	Damascus	187	85.4%
Residential place	Other	32	14.7%
Place of birth	Damascus	125	57.1%
Place of birth	Other	94	42.9%
severe family problems	Yes	30	13.7%
severe family problems	No	189	86.3%
severe psychological trauma	Yes	97	44.3%
severe psychological trauma	No	122	55.7%

Descriptive analysis of Internet addiction variables ( 2nd section questionnaire)

The addiction questionnaire, consisting of (17) statements, was designed on the basis of a three-way Likert scale, which is considered one of the best methods for measuring attitudes. The answer method was for the participant to choose one of the three answers (yes, a little, no). yes = 3, a little = 2; no = 1. The lowest score is 17, and the highest score is 51. As presented in Table 4.

Table 4

Descriptive analysis of Internet addiction variables
	A little		No		Yes
Wasting study time due to the Internet	127	58.0%	17	7.8%	75	34.2%
The effect of excessive Internet use on grades	91	41.6%	42	19.2%	86	39.3%
Difficulty stopping using the Internet, even with fatigue and drowsiness	58	26.5%	98	44.7%	63	28.8%
Suffering from blackout symptoms when you stop using the Internet	25	11.4%	114	52.1%	80	36.5%
There is an urgent desire to return to the Internet after it is closed	83	37.9%	39	17.8%	97	44.3%
Thinking about the Internet while studying	42	19.2%	95	43.4%	82	37.4%
Get more attention online than in real life	47	21.5%	118	53.9%	54	24.7%
Difficulty concentrating while studying and escaping from it to the virtual world	72	32.9%	62	28.3%	85	38.8%
Family problems due to spending a long time on the Internet and neglecting studies	54	24.7%	105	47.9%	60	27.4%
Previous failed attempts to control Internet use	64	29.2%	101	46.1%	54	24.7%
Absence from lectures due to your preoccupation with the Internet	5	2.3%	201	91.8%	13	5.9%
The ability to study for two hours continuously without wanting to open the Internet	126	57.5%	46	21.0%	47	21.5%
Academic degrade due to excessive Internet use	51	23.3%	95	43.4%	73	33.3%
Difficulty concentrating with the lecturer due to the intense desire to access the Internet	18	8.2%	163	74.4%	38	17.4%
Staying up late because of the internet	93	42.5%	56	25.6%	70	32.0%
Being busy with the Internet limits spending enough time with the family	40	18.3%	87	39.7%	92	42.0%
Feeling remorse after spending long time online	87	39.7%	75	34.2%	57	26.0%

Descriptive analysis of secondary variables (3rd section of the questionnaire)

Table 5 presents the descriptive analysis of the secondary variables of the research, where the most frequently used social networking site was Facebook (65.3%). Social networking sites also take up (43.4%) of the sample two or more hours of their time daily. The major of hours allocated to study was two hours or more (42.5%).

Table 5

Descriptive analysis of secondary variables
		Frequency	Percentage
The social networking site most spent on	Facebook	143	65.3%
	Instagram	46	21.0%
	YouTube	30	13.7%
Hours spent on social networking sites	2 ≤ hours	95	43.4%
	6 ≤ hours	38	17.4%
	4 ≤ hours	86	39.3%
Study hours per day	2 ≤ hours	93	42.5%
	6 ≤ hours	23	10.5%
	4 ≤ hours	103	47.0%

Study of Internet addiction index

The value of the addiction index was calculated by replacing one of the three answers (no, a little, yes) with the values (1–2–3). Table 6 presents the analysis of the Internet addiction index, where the value ranged between 17–46, and was mean (34.1), with a standard deviation of 5.1.

Table No. 6 - Calculating the value of the internet addiction index
	Frequency	Minimum value	Supreme value	Mean	standard deviation
addiction index	219	17.00	46.00	34.1963	5.19463
Valid N (listwise)	219

Table 7 presents the percentage of people who are addicted to the Internet and those who are not. The percentage of addiction to the Internet was (60.3%), while there was no addiction to the Internet (39.7%). The percentage of addiction to the Internet was also distributed according to the Gender variable, where the number of males addicted to the Internet was equal to (19.2%), while the number of females addicted to the Internet was equal to (20.5%).

Table No. 7 - Percentage of Internet addiction within the sample
	Frequency	Percentage	Real Percentage	Cumulative Percentage
Not addicted	87	39.7	39.7	39.7
Internet addicted	132	60.3	60.3	100.0
Total	219	100.0	100.0
	Gender
	Female		Male
Not addicted	45	20.5%	42	19.2%
Internet addicted	80	36.5%	52	23.7%

Inferential analysis

Studying the correlation between the degree of addiction and age, academical average percentage, and number of siblings

The Spearman test was conducted to study the correlation between the value of the addiction index and each of the quantitative variables (age, academical average percentage, and number of siblings).

There was a significant correlation between the Internet addiction index and the academical average percentage, but this relationship was negative, that is, the higher the academical average percentage, the lower the Internet addiction.

Table No. 8 - The correlation between the Internet addiction index with age, academical average percentage, and number of siblings
			Correlation value	Significance level value	Test significance
Spearman's correlation	Internet addiction index	Age	.323	.067	No significant difference
		Academical percentage	.432-	.015	Significant difference
		number of siblings	.830	.053	No significant difference

Studying the correlation between the degree of addiction and gender, smoking, current residence, and the presence of (severe family problems) or (severe previous traumas in life)

The Mann-Whitney U test was conducted to study the relationship between gender, smoking, housing, family problems, and previous traumas with the feeling of Internet addiction. Table 9 shows the results of this test.

A significant difference was found in the value of the Internet addiction index with the presence of severe family problems, as the value of the significance level was smaller than (0.05), and the same was true between the value of the Internet addiction index and the presence of severe traumas (0.016).

Table No. 9 - correlation between addiction and gender, smoking, housing, the presence of severe family problems, or psychological traumas
Variables		Frequency	Average rank	Total rank	Value of z	Significance level value	Significance of the test
Gender	Female	125	113.41	14176.50	− .921	.357	No significant difference
	Male	94	105.46	9913.50
	Total	219
Smoking	Yes	72	106.02	7633.50	− .652	.514	No significant difference
	No	147	111.95	16456.50
	Total	219
Residence	Damascus	187	109.47	20470.00	− .020	.984	No significant difference
	other	31	109.71	3401.00
	Total	218
family problems	Yes	30	53.18	1595.50	-5.300	.000	Significant difference between variables
	No	189	119.02	22494.50
	Total	219
severe traumas	Yes	97	98.49	9553.50	-2.403	.016	Significant difference between variables
	No	122	119.15	14536.50
	Total	219

Studying the correlation between the degree of addiction and the academic year, the parents’ education

Table 10 summarizes the result of the Kruskal-Wallis test to study the correlation between academic year, father’s education, and mother’s education with the value of the Internet addiction index. There was no significant difference in the values of the Internet addiction index with academic year (P = 0.390), father’s education (P = 0.474), and mother’s education (P = 0.586).

Table 10

The correlation between the addiction index and the academic year of school, the parents’ education
Variables		frequency	Average rank	chi square value	Significance level value	Significance of the test
Academic year	4th year	50	109.07	1.883	.390	No significant difference
	5th year	71	118.08
	6th year	98	104.62
	Total	219
Father’s education	University	162	108.84	1.494	.474	No significant difference
	Secondary	40	107.11
	Preparatory	17	127.85
	Total	219
Mother’s education	University	133	108.89	1.069	.586	No significant difference
	Secondary	59	107.18
	Preparatory	27	121.61
	Total	219

In our study, the age ranged between 22–28 years, the percentage of females (57.1%) was greater than that of males, and the percentage of sixth-year students was the largest (44.7%). The majority of them were non-smokers, and the academic average percentile was about (78.8%). As for the level of education of the mother and father, the majority of the sample had a university level. The majority of the sample was born in Damascus (47.1%), and the vast majority still lives in Damascus (85.4%). The majority of the sample did not have any severe family problems (13.1%) or previous traumas in their lives (44.3%).

By studying the index of addiction to the Internet, it was found that the prevalence of addiction to the Internet in the sample studied reached (34.2%).

The inferential analysis in our study showed a significant correlation between Internet addiction and the presence of family problems (P = 0.00), with previous traumas in life (P = 0.016), and with the first semester grade point average (P = 0.015). While we did not find a significant correlation with the rest of the variables.

Recommendations

Based on the above, it is recommended to Conduct further studies to investigate risk factors for Internet addiction among medical students. And for Universities to adopt effective policies to confront this problem among their students.

As evidenced by this brief study, the field of Internet addiction is advancing rapidly even without formal recognition of it as a separate and distinct behavioral addiction and with continuing disagreement over diagnostic criteria. The debate over whether IAD should be classified as a (behavioral) addiction, an impulse control disorder, or even obsessive-compulsive disorder cannot be satisfactorily resolved. But the symptoms we have observed in clinical practice show a great deal of overlap with symptoms typically associated with (behavioral) addiction.

It also remains unclear to this day whether the underlying mechanisms responsible for addictive behavior are the same in different types of IAD (e.g., online gaming, excessive browsing).

Ethics approval and consent to participate.

Syrian Private University Ethics committee provided the Ethical approval for the study (IRB-MNNF245), and we confirm that all experiments were performed in accordance with the Declaration of Helsinki. The participants inform their consent to participate in the study.

Consent for publication

The informed consent was obtained from all subjects.

Hayhurst, Christina J. and Durieux, Marcel E , “What is Addiction?” Psychology Today. No date shown. Accessed August 15, 2016.
Kranzler HR, Li TK. What is addiction? Alcohol Res Health. 2008;31(2):93-5. [PMC free article]
Grant JE, Potenza MN, Weinstein A, Gorelick DA. Introduction to behavioral addictions. Am J Drug Alcohol Abuse. 2010 Sep;36(5):233-41. [PMC free article] [PubMed]
Shaffer HJ, LaPlante DA, LaBrie RA, Kidman RC, Donato AN, Stanton MV. Toward a syndrome model of addiction: multiple expressions, common etiology. Harv Rev Psychiatry. 2004 Nov-Dec;12(6):367-74. doi: 10.1080/10673220490905705. PMID: 15764471.
Blum K, Oscar-Berman M, Demetrovics Z, Barh D, Gold MS. Genetic Addiction Risk Score (GARS): molecular neurogenetic evidence for predisposition to Reward Deficiency Syndrome (RDS). Mol Neurobiol. 2014 Dec;50(3):765-96. [PMC free article] [PubMed]
Koob GF, Le Moal M. Drug addiction, dysregulation of reward, and allostasis. Neuropsychopharmacology. 2001 Feb;24(2):97-129. [PubMed]
Nestler EJ, Barrot M, Self DW. DeltaFosB: a sustained molecular switch for addiction. Proc Natl Acad Sci U S A. 2001 Sep 25;98(20):11042-6. [PMC free article] [PubMed]
Perrotti LI, Hadeishi Y, Ulery PG, Barrot M, Monteggia L, Duman RS, Nestler EJ. Induction of deltaFosB in reward-related brain structures after chronic stress. J Neurosci. 2004 Nov 24;24(47):10594-602. [PMC free article] [PubMed]
Torres-Berrio A, Cuesta S, Lopez-Guzman S, Nava-Mesa MO. Interaction Between Stress and Addiction: Contributions From Latin-American Neuroscience. Front Psychol. 2018;9:2639. [PMC free article] [PubMed]
Harford TC, Grant BF, Yi HY, Chen CM. Patterns of DSM-IV alcohol abuse and dependence criteria among adolescents and adults: results from the 2001 National Household Survey on Drug Abuse. Alcohol Clin Exp Res. 2005 May;29(5):810-28. [PubMed]
Weaver MF, Hopper JA, Gunderson EW. Designer drugs 2015: assessment and management. Addict Sci Clin Pract. 2015 Mar 25;10:8. [PMC free article] [PubMed]
Egan KL, Suerken CK, Reboussin BA, Spangler J, Wagoner KG, Sutfin EL, Debinski B, Wolfson M. K2 and Spice use among a cohort of college students in southeast region of the USA. Am J Drug Alcohol Abuse. 2015;41(4):317-22. [PMC free article] [PubMed]
Meyer R, Patel AM, Rattana SK, Quock TP, Mody SH. Prescription opioid abuse: a literature review of the clinical and economic burden in the United States. Popul Health Manag. 2014 Dec;17(6):372-87. [PMC free article] [PubMed]
Gould TJ. Addiction and cognition. Addict Sci Clin Pract. 2010 Dec;5(2):4-14. [PMC free article]
Shippenberg TS, Zapata A, Chefer VI. Dynorphin and the pathophysiology of drug addiction. Pharmacol Ther. 2007 Nov;116(2):306-21. [PMC free article] [PubMed]
Volkow ND, Fowler JS, Wang GJ. The addicted human brain: insights from imaging studies. J Clin Invest. 2003 May;111(10):1444-51. [PMC free article] [PubMed]
Samet S, Waxman R, Hatzenbuehler M, Hasin DS. Assessing addiction: concepts and instruments. Addict Sci Clin Pract. 2007 Dec;4(1):19-31. [PMC free article] [PubMed]
Lacey SJ, Street TD. Measuring healthy behaviours using the stages of change model: an investigation into the physical activity and nutrition behaviours of Australian miners. Biopsychosoc Med. 2017;11:30. [PMC free article] [PubMed]
American Psychiatric Association. Diagnostic and statistical manual of mental disorders. (4th ed., text rev.) Washington, DC: 2000. Author. [Google Scholar]
Young KS. Internet addiction: The emergence of a new clinical disorder. 104th annual meeting of the American Psychological Association; August 11 1996; Toronto, Canada. [Google Scholar]
American Psychiatric Association. DSM-5 Publication Date Moved to May 2013. 2009 [cited 2011 August 21]; [Press release]. Available from: http: //www.psych.org/MainMenu/Newsroom/ NewsReleases/2009NewsReleases/DSM-5-Publication-Date- Moved-.aspx .
Block JJ. Issues for DSM-V: Internet addiction. The American Journal of Psychiatry. 2008 Mar;165(3 ):306–7. [Editorial] [PubMed] [Google Scholar]
Pies R. Should DSM-V designate "Internet addiction" a mental disorder? Psychiatry. 2009 Feb;6(2 ):31–7. [PMC free article] [PubMed] [Google Scholar]
O'Brien CP. Commentary on Tao et al. (2010): Internet addiction and DSM-V. Addiction. [Comment/Reply] 2010 Mar;105(3 ):565. [Google Scholar]
Beard KW. Internet addiction: a review of current assessment techniques and potential assessment questions. CyberPsychology & Behavior. 2005 Feb;8(1 ):7–14. [PubMed] [Google Scholar]
Young KS. Caught in the net: how to recognize the signs of Internet addiction and a winning strategy for recovery. New York: J. Wiley; 1998. [Google Scholar]
Young KS. Internet addiction: the emergence of a new clinical disorder. CyberPsychology & Behavior. 1998 Fal ;1(3 ):237–44. [Google Scholar]
Kratzer S, Hegerl U. Is "Internet Addiction" a disorder of its own? A study on subjects with excessive internet use. Psychiatrische Praxis. 2008 Mar;35(2 ):80–3. [PubMed] [Google Scholar]
Grant JE, Potenza MN, Weinstein A, Gorelick DA. Introduction to behavioral addictions. The American Journal of Drug and Alcohol Abuse. 2010 Aug;36(5 ):233–41. [PMC free article] [Google Scholar]
American Society of Addiction Medicine. Public Policy Statement: Definition of Addiction. 2011 [cited 2011 August 21]; http: //www.asam.org/1DEFINITION_OF_ ADDICTION_LONG_4-11.pdf. Public Policy Statement: Definition of Addiction. 2011 [cited 2011 Augus.
Davis RA. A cognitive behavioral model of pathological internet use (PIU) Computers in Human Behavior. 2001;17(2 ):187–95. [Google Scholar]
Dowling NA, Quirk KL. Screening for Internet dependence: Do the proposed diagnostic criteria differentiate normal from dependent Internet use? CyberPsychology & Behavior. 2009 Feb;12(1 ):21–7. [PubMed] [Google Scholar]
Caplan SE. Problematic Internet use and psychosocial well-being: development of a theory-based cognitive-behavioral measurement instrument. Computers in Human Behavior. 2002;18(5 ):553–75.
Winkler A, Dörsing B. Treatment of internet addiction disorder: a first meta-analysis [Diploma thesis] Marburg: University of Marburg; 2011. [Google Scholar]
JORDANNA KALKHOF, 5 Types of Internet Addiction, June 9, 2020.
Beard KW. Internet addiction: a review of current assessment techniques and potential assessment questions. CyberPsychology & Behavior. 2005 Feb;8(1 ):7–14. [PubMed] [Google Scholar]
Demetrovics Z, Szeredi B, Rozsa S. The three-factor model of Internet addiction: the development of the Problematic Internet Use Questionnaire. Behavior Research Methods. 2008;40(2 ):563–74. [PubMed] [Google Scholar]
Meerkerk G, Van Den Eijnden R, Vermulst A, Garretsen H. The Compulsive Internet Use Scale (CIUS): some psychometric properties. CyberPsychology & Behavior. 2009 Feb;12(1 ):1–6. [PubMed] [Google Scholar]
Linden DJ. The Compass of Pleasure: How Our Brains Make Fatty Foods, Orgasm, Exercise, Marijuana, Generosity, Vodka, Learning, and Gambling Feel So Good. Viking Adult. 2011.
Gabor Maté MD. In the Realm of Hungry Ghosts: Close Encounters with Addiction. North Atlantic Books. 2010.
Bai Y-M, Lin C-C, Chen J-Y. Internet Addiction Disorder Among Clients of a Virtual Clinic. Psychiatric Services. 2001;52(10 ):1397. [Letter] [PubMed] [Google Scholar]
Ko C-H, Liu G-C, Hsiao S, Yen J-Y, Yang M-J, Lin W-C, et al. Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research. 2009;43(7 ):739–47. [PubMed]
Young KS, Nabuco de Abreu C. Internet Addiction: A handbook and guide to evaluation and treatment. New Jersey: John Wiley & Sons Inc; 2011. [Google Scholar]
michai-Hamburger Y, Ben-Artzi E. Loneliness and Internet use. Computers in Human Behavior. 2003;19(1 ):71–80. [Google Scholar]
Eisen S, Lin N, Lyons M, Scherrer J, Griffith K, True W, et al. Familial influences on gambling behavior: an analysis of 3359 twin pairs. Addiction. 1998 Sep;1998:1375–84. [PubMed] [Google Scholar]
Grant JE, Brewer JA, Potenza MN. The neurobiology of substance and behavioral addictions. CNS Spectrums. 2006. 2006 Dec;11(12 ):924–30. [PubMed] [Google Scholar]
Dong G, Lu Q, Zhou H, Zhao X. Precursor or sequela: pathological disorders in people with Internet addiction disorder. Public Library of Science One [serial on the Internet] 2011;6(2 ) Available from: http: //www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal. pone.0014703 . [PMC free article] [PubMed] [Google Scholar]
Petersen KU, Weymann N, Schelb Y, Thiel R, Thomasius R. Pathological Internet use - epidemiology, diagnostics, co-occurring disorders and treatment. Fortschritte Der Neurologie Psychiatrie. [Review] 2009 May;77(5 ):263–71. [PubMed] [Google Scholar]
Young KS. Internet Addiction: Symptoms, Evaluation, And Treatment. Innovations in Clinical Practice [serial on the Internet]. 1999;17 Available from: http: //treatmentcenters.com/downloads/ internet-addiction.pdf . [Google Scholar]
Arisoy O. Internet addiction and its treatment. Psikiyatride Guncel Yaklasimlar. 2009;1(1 ):55–67. [Google Scholar]
Atmaca M. A case of problematic Internet use successfully treated with an SSRI-antipsychotic combination. Progress in Neuro-Psychopharmacology & Biological Psychiatry. 2007 May;31(4 ):961–2. [Letter] [PubMed] [Google Scholar]
Huang X-q, Li M-c, Tao R. Treatment of Internet addiction. Current Psychiatry Reports. 2010 Oct ;12(5 ):462–70. [PubMed] [Google Scholar]
Sattar P, Ramaswamy S. Internet gaming addiction. Canadian Journal of Psychiatry. 2004 Dec;49(12 ):871–2. [Google Scholar]
Wieland DM. Computer addiction: implications for nursing psychotherapy practice. Perspectives in Psychiatric Care. 2005 Oct-Dec;41(4 ):153–61. [PubMed] [Google Scholar]
Dell'Osso B, Hadley S, Allen A, Baker B, Chaplin WF, Hollander E. Escitalopram in the treatment of impulsive-compulsive Internet usage disorder: an open-label trial followed by a double-blind discontinuation phase. Journal of Clinical Psychiatry. 2008 Mar;69(3 ):452–6. [Google Scholar]
Han DH, Hwang JW, Renshaw PF. Bupropion sustained release treatment decreases craving for video games and cue-induced brain activity in patients with Internet video game addiction. Experimental and Clinical Psychopharmacology. 2010 Aug;18(4 ):297–304. [PubMed] [Google Scholar]
Han DH, Lee YS, Na C, Ahn JY, Chung US, Daniels MA, et al. The effect of methylphenidate on Internet video game play in children with attention-deficit/hyperactivity disorder. Comprehensive Psychiatry. 2009 May-Jun;50(3 ):251 –6. [PubMed] [Google Scholar]
Shapira NA, Goldsmith TD, Keck PE , Jr, Khosla UM, McElroy SL. Psychiatric features of individuals with problematic Internet use. Journal of affective disorders. 2000 Jan-Mar;57(1-3 ):267–72. [PubMed] [Google Scholar]
Greenfield DN. Suchtfalle Internet. Hilfe fuer Cyberfreaks, Netheads und ihre Partner. Virtual addiction: Zuerich: Walter. 2000.
Lanjun Z. The applications of group mental therapy and sports exercise prescriptions in the intervention of Internet addiction disorder. Psychological Science (China) 2009 May;32(3 ):738–41.
iller WR, Rollnick S. In: Motivational interviewing: preparing people for change. 2nd ed. Miller WR, Rollnick S, editors. New York: Guilford Press; 2002. [Google Scholar]
Miller NH. Motivational interviewing as a prelude to coaching in healthcare settings. Journal of Cardiovascular Nursing. 2010 May-Jun;25(3 ):247–51. [PubMed] [Google Scholar]
Burke BL, Arkowitz H, Menchola M. The efficacy of motivational interviewing: a meta-analysis of controlled clinical trials. Journal of consulting and clinical psychology. 2003 Oct;71(5 ):843–61. [PubMed] [Google Scholar]
Kim J-U. A reality therapy group counseling program as an Internet addiction recovery method for college students in Korea. International Journal of Reality Therapy. 2007 Spr ;26(2 ):3–9.
Kim J-U. The effect of a R/T group counseling program on the Internet addiction level and self-esteem of Internet addiction university students. International Journal of Reality Therapy. 2008 Spr; 27(2 ):4–12. [Google Scholar]
Widyanto L, Griffiths MD. 'Internet addiction': a critical review. International Journal of Mental Health and Addiction. 2006 Jan;4(1 ):31–51. [Google Scholar]
Abreu CN, Goes DS. Psychotherapy for Internet addiction. In: Young KS, de Abreu CN, editors. Internet addiction: A handbook and guide to evaluation and treatment. Hoboken, NJ: John Wiley & Sons Inc; US; 2011. pp. 155–71. [Google Scholar]
Young KS. Cognitive behavior therapy with Internet addicts: treatment outcomes and implications. CyberPsychology & Behavior. 2007 Oct;10(5 ):671–9. [PubMed] [Google Scholar]
Orzack MH, Orzack DS. Treatment of computer addicts with complex co-morbid psychiatric disorders. Cyberpsychology & Behavior. 1999;2(5 ):465–73. [PubMed] [Google Scholar]
Du Y-s, Jiang W, Vance A. Longer term effect of randomized, controlled group cognitive behavioural therapy for Internet addiction in adolescent students in Shanghai. Australian and New Zealand Journal of Psychiatry. 2010;44(2 ):129–34. [PubMed] [Google Scholar]
Fang-ru Y, Wei H. The effect of integrated psychosocial intervention on 52 adolescents with Internet addiction disorder. Chinese Journal of Clinical Psychology. 2005 Aug;13(3 ):343–5. [Google Scholar]
Rong Y, Zhi S, Yong Z. Comprehensive intervention on Internet addiction of middle school students. Chinese Mental Health Journal. 2006 Jul;19(7 ):457–9. [Google Scholar]
Neha Alawadhi, IAMAI report. ET Bureau Sep 3, 2015. Available form: http://articles.economictimes.indiatimes.com/2015-09-03/news/66178659_1_user-base-iamaiinternet-and-mobile-association .
Widyanto L, Griffiths M. Internet addiction: A critical review. Int J Ment Health Addict. 2006;4:31–51. [Google Scholar]
Chou C, Condron L, Belland JC. A review of the research on internet addiction. Educ Psychol Rev. 2005;17:363–88. [Google Scholar]
Young KS, Rogers RC. The relationship between depression and internet addiction. Cyberpsychol Behav. 1998;1:25–8. [Google Scholar]
Sharma A, Sahu R, Kasar PK, Sharma R. Internet addiction among professional courses students: A study from Central India. Int J Med Sci Public Health. 2014;3:1069–73. [Google Scholar]
Kandell JJ. Internet addiction on campus: The vulnerability of college students. Cyberpsychol Behav. 1998;1:11–7. [Google Scholar]
Chakraborty K, Basu D, Kumar K. Internet addiction: Consensus, controversies, and the way ahead. East Asian Archives of Psychiatry. 2010 Sep;20(3 ):123–32. [PubMed] [Google Scholar]
Peukert P, Sieslack S, Barth G, Batra A. Internet- and computer game addiction: Phenomenology, comorbidity, etiology, diagnostics and therapeutic implications for the addictives and their relatives. Psychiatrische Praxis. 2010 Jul;37(5 ):219–24. [PubMed] [Google Scholar]
Weinstein A, Lejoyeux M. Internet addiction or excessive Internet use. The American Journal of Drug and Alcohol Abuse. 2010 Aug;36(5 ):277–83. [PubMed] [Google Scholar]
Young KS, Nabuco de Abreu C. Internet Addiction: A handbook and guide to evaluation and treatment. New Jersey: John Wiley & Sons Inc; 2011. [Google Scholar]
Winkler A, Dörsing B. Treatment of internet addiction disorder: a first meta-analysis [Diploma thesis] Marburg: University of Marburg; 2011. [Google Scholar]
Haroon MZ, Zeb Z, Javed Z, Awan Z, Aftab Z, Talat W. Internet Addiction In Medical Students. J Ayub Med Coll Abbottabad. 2018 Oct-Dec;30(Suppl 1)(4):S659-S663. PMID: 30838826.
Taha MH, Shehzad K, Alamro AS, Wadi M. Internet Use and Addiction Among Medical Students in Qassim University, Saudi Arabia. Sultan Qaboos Univ Med J. 2019 May;19(2):e142-e147. doi: 10.18295/squmj.2019.19.02.010. Epub 2019 Sep 8. PMID: 31538013; PMCID: PMC6736271.
Nath K, Naskar S, Victor R. A Cross-Sectional Study on the Prevalence, Risk Factors, and Ill Effects of Internet Addiction Among Medical Students in Northeastern India. Prim Care Companion CNS Disord. 2016 Mar 31;18(2):10.4088/PCC.15m01909. doi: 10.4088/PCC.15m01909. PMID: 27486546; PMCID: PMC4956431.

The authors declare no competing interests.

Download PDF

Version 1

posted

You are reading this latest preprint version

Medical Students' Internet Addiction And Its Impact On Academic Achievement

Status:

Version 1

Abstract

Introduction

Methods and Materials

Results

Discussion

Conclusion

Introduction

Material and Methods

Aimes

Study Design

Data collection tool

Approvals:

Statistical Analyses

Results

Internal consistency test for the questionnaire:

Testing the normal distribution of data:

Demographic variables of the sample (1st section of questionnaire)

Descriptive analysis of Internet addiction variables ( 2nd section questionnaire)

Descriptive analysis of secondary variables (3rd section of the questionnaire)

Study of Internet addiction index

Inferential analysis

Studying the correlation between the degree of addiction and the academic year, the parents’ education

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1