Gender Difference in Internet Use and Internet Problems among Quebec High School Students

Abstract

French

Objectives:

There are presently no data available concerning Internet addiction (IA) problems among adolescents in Canada and the province of Quebec. The goal of this study is thus to document and compare the influence of gender on Internet use and addiction.

Method:

The study data were collected from a larger research project on gambling among adolescents. Activities conducted online (applications used and time spent) as well as answers to the Internet Addiction Test (IAT) were collected from 3938 adolescents from grades 9 to 11. The two most often employed cut-off points for the IAT in the literature were documented: (40-69 and 70+) and (50+).

Results:

Boys spent significantly more time on the Internet than did girls. A greater proportion of the girls made intense use of social networks, whereas a greater proportion of the boys made intense use of massively multiplayer online role-playing games, online games, and adult sites. The proportion of adolescents with a potential IA problem varied according to the cut-off employed. When the cut-off was set at 70+, 1.3% of the adolescents were considered to have an IA, while 41.7% were seen to be at risk. At a 50+ cut-off, 18% of the adolescents were considered to have a problem. There was no significant difference between the genders concerning the proportion of adolescents considered to be at risk or presenting IA problems. Finally, analysis of the percentile ranks would seem to show that a cut-off of 50+ better describes the category of young people at risk.

Conclusions:

The results of this study make it possible to document Internet use and IA in a large number of Quebec adolescents.

Keywords

Internet addiction Internet applications adolescents gender

Clinical Implications

With the more severe cut-off point employed, between 1.16% and 1.45% adolescents have an Internet problem. It is therefore important that they have access to treatment.

A significant proportion of adolescents are probably at risk of developing addiction problems. Careful thought must be given to prevention for these young people. This prevention should be addressed specifically according to gender.

The percentile ranks seem to indicate that new cut-off points should be employed to determine at-risk categories. The score of 50 (instead of 40) seemed appropriate in this study.

Limitations

Despite a sizable number of participants, the use of a convenience sample limits the generalization of the results.

The lack of a gold standard to which we could compare the IAT performance is a serious weak point in this field of research.

The rapid development of software applications and the growing use of the Internet raise numerous questions about the repercussions of this technology in the life of adolescents.^1,2 While most adolescents make reasonable use of the Internet, some lose control and their use becomes so excessive that it is often qualified as Internet addiction (IA).^3,4

There is as of yet no consensus regarding the conceptualization or operationalization of IA.^5

–10 Despite these debates, several studies have reported IA prevalences oscillating between 0.6% and 26.7%.^{4,10

–18} These sizable variations in the prevalences—depending on the studies, the countries, samples, and the evaluation criteria^10,19,20—are of note.

Numerous studies also highlight the differences between genders in terms of both application utilization and the prevalence of IA problems.^4,12,14 The prevalence of IA problems would seem to be higher in boys than in girls.^4,12,14

There are presently no data available concerning IA problems among adolescents in the province of Quebec.¹⁹ These data are of interest considering that the cultural differences associated with young Quebecers may have an impact on Internet use.^21,22 Furthermore, numerous health practitioners wonder whether preventive intervention is needed.^2,23,24 The goal of the present study is to document Internet use and the proportion of Internet addiction according to gender.

Method

Participants

The study data were collected in 2012 during a research project on gambling among adolescents.²⁵ A convenience sampling method was employed for schools selection. Fourteen public and private secondary schools were approached, and 11 agreed to participate. Participants in grades 9, 10, and 11 were invited to complete the questionnaires individually in their classroom for a duration of 50 minutes.

Instruments

For Internet activities, the time spent on 9 applications was documented: social networks (Facebook, Twitter, etc.), YouTube, chatroom (MSN, Myspace, other than Facebook), blogs or discussion forums, news sites (Cyberpresse, Canoe), massively multiplayer online role-playing games or MMORPGs (World of Warcraft, League of Legends), online games with other players (Call of Duty, Counter Strike), downloading music or films, and visiting adult sites (XXX). For each of the applications and excluding time spent for schoolwork, they had to evaluate how many hours they spent online using a 7-point Likert scale (1 = 0 minutes, 2 = less than an hour, 3 = 5 hours, 4 = 15 hours, 5 = 20 hours, 6 = 30 hours, 7 = 40+ hours). Considering that more than 20 hours of Internet per week is high-intensity use,²⁶ time spent online was put into 2 categories: high intensity (20 hours or more) and low intensity (19 hours or less).

Internet addiction was measured with the French version²⁷ of the Internet Addiction Test (IAT).²⁸ This 20-item questionnaire was evaluated using a 5-point Likert scale and the scores varied from 20 to 100. The IAT scale was shown to have an internal consistency coefficient of 0.93. Although this scale has been employed in many studies,²⁹ different cut-off points have been used to determine which people are at risk or addicted.^4,10,27,30 Given the lack of sufficient consensus, we employed the 2 most common ways of categorizing the IAT scores: 1) by using the 3 categories recommended by Young^3,13,28 (no risk [20-39], at risk [40-69] or probable Internet addict [70-100]) and 2) by using 2 categories^27,31–33 (average users [20-49] or problematic users [50 or more]).

Data Analysis

Pearson’s chi-square test and Fisher’s exact test were used to compare the 2 groups’ means and proportions. Percentile scores of the IAT results were calculated for each gender. Due to the sample size, the significance level at <0.0001 was used to reduce the possibility for type II errors.³⁴

Results

We surveyed a total of 3938 high school students, with a response rate of 98%. Participants were 15.3 years old on average (range, 14-21 years old), of whom 43.7% were boys and 56.3% were girls. Almost all participants (88.8%) spoke French at home, and 87.8% were Canadian. Thirty-one percent of the sample were in grade 9, 36.1% in grade 10, and 32.44% in grade 11.

Intensity of Time Spent on Each Internet Activity

On average, the boys spent significantly more time on the Internet each week (19.65 hours, SD = 17.70) than did the girls (16.68 hours, SD = 15.13) (<0.0001).

Comparing the intensity of the time spent on Internet activities, a greater proportion of the girls made intense use of social networks (P < 0.0001), whereas a greater proportion of the boys made intense use of MMORPGs (P < 0.0001), online games (P < 0.0001), and adult sites (P < 0.0001) (see Table 1).

Table 1.

Intensity of Time Spent on Internet Activities Each Week According to Gender.

Applications	Boys, % (n)	Girls, % (n)	P Value
Social networks (n = 3880)
≤19 hours	89.91 (1527)	83.11 (1812)	<0.0001
≥20 hours	10.09 (172)	16.89 (369)
YouTube (n = 3882)
≤19 hours	92.10 (1562)	93.85 (2052)	0.0326
≥20 hours	7.90 (135)	6.15 (133)
Chat (n = 3871)
≤19 hours	96.88 (1635)	95.17 (2077)	0.007
≥20 hours	3.125 (53)	4.83 (106)
Blogs (n = 3871)
≤19 hours	99.29 (1681)	98.95 (2155)	0.25
≥20 hours	0.70 (12)	1.05 (23)
News (n = 3870)
≤19 hours	99.65 (1684)	99.86 (2177)	0.16
≥20 hours	0.35 (6)	0.14 (3)
MMORPGs (n = 3883)
≤19 hours	90.23 (1529)	99.41 (2173)	<0.0001
≥20 hours	9.77 (168)	0.59 (13)
Online games (n = 3874)
≤19 hours	87.30 (1481)	98.85 (2156)	<0.0001
≥20 hours	12.70 (215)	1.05 (22)
Downloading music or films (n = 3876)
≤19 hours	97.13 (1648)	97.90 (2134)	0.1239
≥20 hours	2.87 (49)	2.10 (45)
Adult sites (n = 3873)
≤19 hours	97.22 (1635)	99.77 (2186)	<0.0001
≥20 hours	2.78 (47)	0.23 (5)

MMORPG, massively multiplayer online role-playing game.

Internet Addiction Problems

The mean IAT score was 39.43 (SD = 11.30) for the boys and 40.03 (SD = 11.70) for the girls, with no difference between genders (P = 0.1234). There was no significant difference between gender with regard to the proportion who were considered at risk or who had IA problems, no matter which cut-off point was employed (see Table 2).

Table 2.

Internet Addiction According to Gender and Cut-Off Point Employed.

Cut-Off	Boys		Girls
Cut-Off	% (95% CI)	n	% (95% CI)	n	Total N	P Value^a
IAT: 3 categories^b
20 to 40: no risk	57.78 (55.43-60.09)	995	56.27 (57.19-58.33)	1243	2238
40 to 69: at risk	41.06 (38.76-43.40)	707	42.28 (40.24-44.35)	934	1641
70 to 100: probable Internet addict	1.16 (0.75-1.79)	20	1.45 (1.03-2.04)	32	52	0.511
IAT: 2 categories^c
20 to 49: average user	81.53 (79.63-83.29)	1404	78.9 (77.15-80.55)	1743	3147
50 to 100: problematic user	18.47 (16.71-20.37)	318	21.1 (19.45-22.45)	466	784	0.040

CI, confidence interval; IAT, Internet Addiction Test.

^aChi-squared.

^bCut-off with 3 categories according to Young.²⁸

^cCut-off with 2 categories according Khazaal et al.²⁷

Percentile Ranks

The classification standards employed in IA studies^35
–37 often set the 95th or 99th percentile as the problem user category, whereas the 75th or 80th percentile is often used to determine the at-risk category. As described in Table 3, the cut-off points corresponding to these percentiles were similar for both genders.

Table 3.

Proposed Cut-Off Criteria of Categories of Internet-Users Based on Percentile.

User Category	Percentile	IAT Score (Boys)	IAT Score (Girls)
At-risk users	75th to 95th	46 to 58	48 to 60
	80th to 95th	48 to 58	50 to 60
Problem users	95th or greater	59+	61
	99th or greater	68+	69

IAT, Internet Addiction Test.

Discussion

To our knowledge, this is the first study to document Internet use and IA problems in a large number of adolescents in Quebec. In comparison with adolescents in other countries, young Quebeckers used the Internet for more hours per week. On average, they spent 17.9 hours compared to weekly averages ranging from 8 to 15 hours elsewhere in the world.^38
–40 Moreover, the number of weekly hours spent on the Internet has increased since 2007, when a similar study found that adolescents spent 11 hours per week.⁴¹ The constant arrival of new applications, notably Facebook, which came online in 2008, might explain this difference.

As expected, the boys spent significantly more time on the Internet and used different applications than did the girls. These results were similar to those observed in other European countries where the boys played online more often (MMORPGs and online games) and went to adult sites more often, while the girls were on social networks more often.^17,18,32

Whereas the score of 40 was set as the cut-off point for determining the at-risk group,^3,18,30 it was also the average result for the girls (40.03) and close to the average result for the boys (39.43). These results on the IAT scale call into question the cut-off point proposed by Young²⁸ for the category of at-risk adolescents. The percentile ranks would seem to indicate that a cut-off of 50 would probably better delimit the category of adolescents at risk. As for the cut-off of 70 regularly used in studies,^3,18,30 it approximately corresponds to the 99th percentile for both genders. It is thus quite likely that it detected the adolescents who were in the greatest difficulty due to their Internet use. A clinical validation of the cut-off points should confirm the relevance of setting the points at 50 or 70.

Contrary to some studies,^4,11,12 there was no difference in the proportion of IA among boys and girls. Furthermore, the prevalence in IA problems varies considerably according to the cut-off point employed. When the three categories recommended by Young²⁸ were used, 41.7% of the adolescents were at risk and 1.32% had IA problems. This level of IA prevalence was lower than that observed in most countries,^{4,11

–15,17,20,42

–45} but the prevalence of the at-risk category was higher than those generally observed.^4,11,12,18 When using the 50+ cut-off point, 19.94% of adolescents were considered problematic, with a prevalence higher than that observed in Italy (5.8%)¹⁶ and in China (8.1%-12.2%)^18,46,47 but lower than that observed among Italian adolescents (36.7%).⁴⁸ The variation in the results according to the cut-off point that was employed highlights the inherent difficulties in conceptualizing and comparing IA prevalence in studies.

Despite the large number of participants, this study was based on a convenience sample, which cannot be considered representative of all Quebec adolescents. Other studies with representative samples of the Quebec and Canadian population will have to be conducted to arrive at a more accurate portrait of IA problems.

Regardless of this study’s limitation, these results would seem to indicate that a small proportion of adolescents has an IA problem and should thus have access to treatment. Furthermore, a large number of adolescents would seem to be at risk of developing addiction problems. Longitudinal studies will be necessary to better understand these young people and determine whether they are truly at risk.^10,22 These observations raise questions about the prevention that should be provided for these young people. Given that their Internet use is different, gender-specific prevention activities should be developed.

Footnotes

Acknowledgments

We thank the all participants and schools that collaborated in this study and all members of the research team. We also want to thank the Centre de réadaptation en dépendance de Montréal, Institut Universitaire for their financial support for the translation of this paper.

Author Note

This research involves human participants. All participants gave explicit consented to take part in the study and ethics approval for the study was provided by the Comité d'éthique de la recherche de la recherche de l'Université du Québec à Trois-Rivières. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) declared receipt of the following financial support for the research, authorship, and/or publication of this article: This research was financed by the Fonds de recherche Société et Culture du Québec (Grant # 164271).

References

Guan

S-SA

Subrahmanyam

. Youth Internet use: risks and opportunities. Curr Opin Psychiatry. 2009;22(4):351–356.

Biron

J-F

Dansereau

. Les préoccupations et les impacts associés à l’utilisation d’Internet dans les milieux des jeunes d’âge scolaire: les relations, le temps et le développement: rapport synthèse. Agence de la santé et des services sociaux de Montréal, Direction de santé publique, Secteurs Tout-petits-Jeunes; 2011.

Bruno

Scimeca

Cava

Pandolfo

Zoccali

Muscatello

. Prevalence of Internet addiction in a sample of southern Italian high school students. Int J Ment Health Addict. 2014;12(6):708–715.

Koruklu

Yılmaz

. A study on Turkish adolescent’s Internet use: possible predictors of Internet addiction. Cyberpsychol Behav Soc Netw. 2013;16(3):205–209.

Czincz

Hechanova

. Internet addiction: debating the diagnosis. J Technol Hum Serv. 2009;27(4):257–272.

Desai

Krishnan-Sarin

Cavallo

Potenza

. Video-gaming among high school students: health correlates, gender differences, and problematic gaming. Pediatrics. 2010;126(6):e1414–e1424.

Villella

Martinotti

Di Nicola

. Behavioural addictions in adolescents and young adults: results from a prevalence study. J Gambl Stud. 2011;27(2):203–214.

Weinstein

Lejoyeux

. Internet addiction or excessive Internet use. Am J Drug Alcohol Abuse. 2010;36(5):277–283.

Hinić

. Problems with “Internet addiction” diagnosis and classification. Psychiatr Danub. 2011;23(2):145–151.

10.

Lopez-Fernandez

Freixa-Blanxart

Honrubia-Serrano

. The Problematic Internet Entertainment Use Scale for Adolescents: prevalence of problem Internet use in Spanish high school students. Cyberpsychol Behav Soc Netw. 2013;16(2):108–118.

11.

Bakken

Wenzel

Götestam

Johansson

Øren

. Internet addiction among Norwegian adults: a stratified probability sample study. Scand J Psychol. 2009;50(2):121–127.

12.

Durkee

Kaess

Carli

. Prevalence of pathological Internet use among adolescents in Europe: demographic and social factors. Addiction. 2012;107(12):2210–2222.

13.

Gencer

Koc

. Internet abuse among teenagers and its relations to Internet usage patterns and demographics. Educ Technol Soc. 2012;15(2):25–36.

14.

Y-M

Hwang

. Gender differences in Internet addiction associated with psychological health indicators among adolescents using a national web-based survey. Int J Ment Health Addict. 2014;12(5):660–669.

15.

Kaltiala-Heino

Lintonen

Rimpelä

. Internet addiction? Potentially problematic use of the Internet in a population of 12-18 year-old adolescents. Addict Res Theory. 2004;12(1):89–96.

16.

Poli

Agrimi

. Internet addiction disorder: prevalence in an Italian student population. Nord J Psychiatry. 2012;66(1):55–59.

17.

Kuss

Griffiths

Karila

Billieux

. Internet addiction: a systematic review of epidemiological research for the last decade. Curr Pharm Des. 2013;1(4):397–413.

18.

Lam

Peng

Mai

Jing

. Factors associated with Internet addiction among adolescents. CyberPsychol Behav. 2009;12(5):551–555.

19.

Dufour

Nadeau

Gagnon

. Tableau clinique des personnes cyberdépendantes demandant des services dans les centres publics de réadaptation en dépendance au Québec: étude exploratoire. Sante Ment Que. Revue Santé mentale au Québec; 2014;39(2):149–168.

20.

Ghassemzadeh

Shahraray

Moradi

. Prevalence of Internet addiction and comparison of Internet addicts and non-addicts in Iranian high schools. CyberPsychol Behav. 2008;11(6):731–733.

21.

Mackey

. The house of difference: cultural politics and national identity in Canada. Toronto (ON): University of Toronto Press; 2002.

22.

Lopez-Fernandez

. How has Internet addiction research evolved since the advent of Internet gaming disorder? An overview of cyberaddictions from a psychological perspective. Curr Addict Reports. 2015;2(3):263–271.

23.

Kern

. Problematic Internet use: perceptions of addiction counsellors. Comput Educ. 2011;56(4):983–989.

24.

Dufour

Gendron

Cousineau

Leclerc

. Adolescent technology use: profiles of distinct groups and associated risky behaviors. J Addict Res Ther. 2014;10:2.

25.

Brunelle

Leclerc

Cousineau

M-M

Dufour

Gendron

Martin

. Internet gambling, substance use, and delinquent behavior: an adolescent deviant behavior involvement pattern. Psychol Addict Behav. 2012;26(2):364.

26.

CEFRIO. Netendances 2011: le commerce électronique et les services bancaires en ligne. Publ Cefrio. 2011;2(6):1–18.

27.

Khazaal

Billieux

Thorens

. French validation of the Internet addiction test. CyberPsychol Behav. 2008;11(6):703–706.

28.

Young

. Internet addiction: the emergence of a new clinical disorder. CyberPsychol Behav. 1998;1(3):237–244.

29.

Lortie

Guitton

. Internet addiction assessment tools: dimensional structure and methodological status. Addiction. 2013;108(7):1207–1216.

30.

Watters

Keefer

Kloosterman

Summerfeldt

Parker

JDA

. Examining the structure of the Internet Addiction Test in adolescents: a bifactor approach. Comput Hum Behav. 2013;29(6):2294–2302.

31.

Berner

Santander

Contreras

Gómez

. Description of Internet addiction among Chilean medical students: a cross-sectional study. Acad Psychiatry. 2014;38(1):11–14.

32.

Yen

J-Y

Yen

C-F

Chen

C-S

Tang

T-C

C-H

. The association between adult ADHD symptoms and Internet addiction among college students: the gender difference. CyberPsychol Behav. 2009;12(2):187–191.

33.

Liberatore

Rosario

Martí

LNC-D

Martínez

. Prevalence of Internet addiction in Latino adolescents with psychiatric diagnosis. Cyberpsychol Behav Soc Netw. 2011;14(6):399–402.

34.

Skipper

Guenther

Nass

. The sacredness of. 05: a note concerning the uses of statistical levels of significance in social science. Am Sociol. 1967;2(1):16–18.

35.

C-H

Yen

J-Y

Chen

C-C

Chen

S-H

Yen

C-F

. Proposed diagnostic criteria of Internet addiction for adolescents. J Nerv Ment Dis. 2005;193(11):728–733.

36.

Chow

Leung

. A screen for identifying maladaptive Internet use. Int J Ment Health Addict. 2009;7(2):324–332.

37.

Jenaro

Flores

Gómez-Vela

González-Gil

Caballo

. Problematic Internet and cell-phone use: psychological, behavioral, and health correlates. Addict Res Theory. 2007;15(3):309–320.

38.

Adiele

Olatokun

. Prevalence and determinants of Internet addiction among adolescents. Comput Hum Behav. 2014;31:100–110.

39.

de Ayala López

MCL

Gutierrez

JCS

Jiménez

. Problematic Internet use among Spanish adolescents: the predictive role of Internet preference and family relationships. Eur J Commun. 2015;30(4):470–485.

40.

Holstein

Pedersen

Bendtsen

. Perceived problems with computer gaming and Internet use among adolescents: measurement tool for non-clinical survey studies. BMC Public Health. 2014;14(1):361.

41.

Dufour

Brunelle

Gendron

Leclerc

Cousineau

M-M

. L’utilisation d’Internet chez les jeunes adolescents au secondaire. Écho-Toxico. 2013;23(1):11–13.

42.

Choi

Son

Park

. Internet overuse and excessive daytime sleepiness in adolescents. Psychiatry Clin Neurosci. 2009;63(4):455–462.

43.

Jang

Hwang

Choi

. Internet addiction and psychiatric symptoms among Korean adolescents. J Sch Health. 2008;78(3):165–171.

44.

Park

Kim

Cho

. Prevalence of Internet addiction and correlations with family factors among South Korean adolescents. Adolescence. 2008;43(172):895.

45.

Hawi

. Internet addiction among adolescents in Lebanon. Comput Hum Behav. 2012;28(3):1044–1053.

46.

Wang

Zhou

Deng

Hong

. Problematic Internet use in high school students in Guangdong province, China. PLoS One. 2011;6(5):e19660.

47.

Cao

Sun

Wan

Hao

Tao

. Problematic Internet use in Chinese adolescents and its relation to psychosomatic symptoms and life satisfaction. BMC Public Health. 2011;11(1):802.

48.

Milani

Osualdella

Di Blasio

. Quality of interpersonal relationships and problematic Internet use in adolescence. CyberPsychol Behav. 2009;12(6):681–684.