HEALTH BEHAVIORS AND USE OF INTERNET TECHNOLOGIES IN ADOLESCENTS (HBSC STUDY RESULTS)

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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES

MEDICAL ACADEMY FACULTY OF MEDICINE

Cristian Escobar García

HEALTH BEHAVIORS AND USE OF INTERNET

TECHNOLOGIES IN ADOLESCENTS

(HBSC STUDY RESULTS)

In the Department of Preventive Medicine

Submitted in partial fulfillment of the requirements for the degree of

Master of Medicine

Scientific supervisor:

Apolinaras Zaborskis, PhD, professor

June 2017 Kaunas

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SUMMARY

Author’s name. Cristian Escobar Garcia.

Title. Health behavior and use of internet technologies in adolescents (HSBC study results). Scientific supervisor: Apolinaras Zaborskis, PhD, professor, Department of Preventive Medicine, Faculty of Public Health, Medical Academy, Lithuanian University of Health sciences

Kaunas; 2017, 39 pages

Background. For the youth, the Internet presents a number of risks along with a multitude of

opportunities. Research suggests that some of the online risks facing young people are addiction, exposure to inappropriate material, cyberbullying, sexual solicitation, and a more sedentary lifestyle. It also suggests that the Internet can be beneficial for young people. It provides a vehicle to promote cognitive, social, and physical development. The Internet can also serve as a health promotion channel. However, data about the relationship between Internet use and health behavior in adolescents is scarce.

Aim. To analyze the relationship between the use of Internet Technologies (IT) and health

behavior in adolescents of different countries.

Objectives. 1. To study how much time adolescents spend on using IT, by gender and age.

2. To analyze whether there is a relationship between IT use and sedentary lifestyle.

3. To analyze whether there is a relationship between IT use and addiction and cyberbullying. 4. To compare data from different countries and see possible differences.

Methods. Database of cross-national Health Behavior in School-aged Children (HBSC)

survey that was conducted in 2013/2014 school year was used for analysis. Adolescents of 11, 13, and 15 years of age were surveyed in 40 countries across Europe, US, Canada, and Israel. The instrument of the survey was an anonymous questionnaire. Data analysis was performed using SPSS package.

Results. Results show that use of IT has a significant impact on adolescents’ lifestyle. Girls

and adolescents of the older age group are usually more active in their IT use. Globally, there is an almost equal distribution between extensive and nonextensive use of IT. There also seems to be a positive relationship between IT use and sedentary lifestyles. IT also seems to increase the risk of substance abuse and in some cases of cyberbullying.

Conclusions. Internet technologies have a significant impact on health behaviors of

adolescents and their lifestyle. Further research and study of this is required in order to try to change the negative outcomes that derive of its use and try to maximize its potential advantages for adolescents’ health.

Keywords. Internet technologies. Electronic media communication. Adolescents. Health.

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SANTRAUKA

Autorius. Cristian Escobar Garcia.

Pavadinimas. Paauglių gyvensena ir internetinių technologijų naudojimas (pagal HBSC

tyrimo duomenis).

Mokslinis vadovas. Apolinaras Zaborskis, habil. dr., profesorius; Lietuvos sveikatos mokslų universitetas, Medicinos akademija, Visuomenės sveikatos fakultetas, Profilaktinės medicinos katedra, Kaunas, 2017; 39 psl.

Aktualumas. Jauniems žmonėms internetas bei kitos šiuolaikinės ryšio priemonės teikia daug

galimybių, bet tuo pačiu sukelia ir įvairių pavojų. Tyrimai patvirtina, kad internetas skatina jaunų žmonių kognityvinį, socialinį ir fizinį vystymąsi; jis gali būti ir sveikatos ugdymo ir stiprinimo žinių šaltinis. Kita vertus, naudodamiesi internetu vaikai ir paaugliai randa daug jiems neleistinos informacijos, kyla priklausomybių, kibernetinių patyčių pavojus, daug laiko praleidžiama pasyviai. Tačiau duomenų apie internetinių technologijų (IT) naudojimo ryšį su sveikata susijusiu elgesiu paauglystėje stokojama. Visai nėra duomenų, kaip šis ryšys pasireiškia skirtingose šalyse.

Tikslas. Ištirti ryšį tarp IT naudojimo ir paauglių elgsenos įvairiose šalyse.

Uždaviniai. 1. Nustatyti, kiek laiko paaugliai praleidžia naudodami IT skirtingose šalyse,

atsižvelgiant į lytį ir amžių.

2. Patikrinti, ar yra ryšys tarp IT naudojimo ir sėdint praleidžiamo laiko.

3. Išanalizuoti ryšį tarp IT naudojimo ir priklausomybę sukeliančių medžiagų vartojimo bei patyčių kibernetinėje erdvėje.

4. Palyginti įvairių šalių duomenis, rasti tiriamos problemos nacionalinius skirtumus.

Metodai. Analizei panaudoti tarptautinio mokyklinio amžiaus vaikų gyvensenos ir sveikatos

tyrimo HBSC, atlikto 2013/2014 mokslo metais, duomenų bazė. Vykdant momentinį tyrimą, 11, 13 ir 15 metų amžiaus paaugliai buvo tirti 40 šalių (Europoje, Kanadoje ir Izraelyje). Duomenys rinkti anoniminės anketinės apklausos metodu. Jų analizei taikyta SPSS programa.

Rezultatai. Tyrimas atskleidė, kad IT naudojimas paauglių veikloje užima svarbią vietą.

Mergaitės bei vyresni paaugliai šiomis priemonėmis naudojasi dažniau. Apskritai, dažnesnis IT naudojimas koreliavo su sėdint praleidžiamo laiko trukme. IT naudojimas didino priklausomybę sukeliančių medžiagų vartojimo bei kibernetinių patyčių riziką.

Išvados. IT naudojimas turi svarbią reikšmę paauglių elgesiui ir gyvensenai. Reikalingi

tolimesni tyrimai, siekiant minimizuoti negatyvų jo poveikį ir maksimaliai panaudoti stiprinant paauglių sveikatą.

Raktažodžiai. Interneto technologijos. Elektroninės ryšio priemonės. Paaugliai. Sveikata.

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CONFLICTS OF INTEREST

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ABBREVIATIONS LIST

Electronic media communication (EMC) Internet Technologies (IT)

Health Behavior in School-aged Children (HBSC) World Health Organization (WHO)

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TERMS

Bullying: unwanted, aggressive behavior among school aged children that involves a real or

perceived power imbalance. The behavior is repeated, or has the potential to be repeated, over time.

Cyberbullying: the use of electronic communication to bully a person, typically by sending

messages of an intimidating or threatening nature.

Electronic media communication: any form of media that uses electronics or that requires

an electromechanical audience to access the content.

Health Behavior in School-aged Children (HBSC): cross-national study gaining insight

into young people’s well-being, health behaviors and their social context.

Health Behaviors: any activity undertaken by a person who believes himself to be healthy

for the purpose of preventing disease or detecting disease in an asymptomatic stage.

Internet Technologies: any form of media capable of using the Internet to transmit and

receive information.

World Health Organization (WHO): specialized agency of the United Nations that is

concerned with international public health. Established on 7 April 1948, headquartered in Geneva, Switzerland.

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1. INTRODUCTION

Adolescents far surpass adults in their use of Internet Technologies (IT) [1] . This fact can’t be understated and as such its implications on adolescents’ health need to be carefully reviewed and studied. The internet offers adolescents a plethora of opportunities for their self-development, such as the chance to establish deep connections with their peers and help in their cognitive and academic development [2]. However, it is also littered with risks, such as the danger of cyber addiction [3], interaction with strangers, visitation of inappropriate websites, unwanted sexual advancements [4] and cyberbullying. Also important to mention is the increased risk for drug addiction [5] and a possible association with a more sedentary lifestyle [6]. Having said all of this, the purpose of our analysis is to expand on this field since we believe that there hasn’t been enough research conducted on the impact ITs have on adolescents’ health behavior. We believe that our topic is very relevant in this day and age given the increased use that youth today make of these technologies. We predict that this use will only increase in the future and thus it is important to study the health impacts these technologies may have. For our study, we have used HSBC results from a cross-national survey conducted in 2013/2014 spanning data collected from 42 countries in Europe and North America from approximately 220 000 young people. This will give us a more global perspective on the issue. Hence, it is rationale to analyze the relationship between EMC and health behaviors in adolescents and try to formulate policies that will improve the health outcomes derived from their use.

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2. AIM AND OBJECTIVES OF THE THESIS

Aim. To study the relationship between the use of Internet Technologies (IT) and health

behaviors in adolescents in different countries.

In order to achieve our aim, we used the HBSC cross-national survey from 2013/2014 and performed some statistical analyses using the program SPSS on a number of variables of our interest. When appropriate, new variables were created using the existing ones in order to help with our study. Using this method, we hope to fulfill the following objectives:

1. To analyze the time spent by adolescents on the computer, by gender and age.

2. To observe possible relationships between IT use and sedentary lifestyles.

3. To observe possible relationships between IT use, addiction, and cyberbullying.

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3. LITERATURE REVIEW

3.1 Electronic media communication and Internet Technologies

Electronic media communication spans from simple phone calling to use of more complex media outlets such as forums and social networking sites. More specifically, we distinguished between 5 categories of Electronic Media Communication (EMC): phone calls, SMS, email, instant messaging, and other sources such as social networking sites, mobile apps to share photos, and so on. Roberts and Foehr, in a study conducted in the US found that adolescents are on average exposed to eight and a half hours of EMC daily. They found that “older” media is not replacing “new” media, but rather that both are complementing each other [7]. Of the social networks, Facebook and Twitter appear to be the most popular based on the number of users they possess. Approximately 67% of all internet users use social media. Young adults are more prone to using these than older ones are [8]. Parents must be aware of the use their kids and adolescents make of these sites. An irresponsible use can lead the child to unhealthy environments in these media that could result in depression, bullying, “sexting”, and exposure to inappropriate content [9].

There has been a change in the way adolescents make use of the Internet. Ever since 2006, blogging use has decreased steadily among teens, while it has remained steady in the adult population. Use of social networking sites has increased dramatically in both teen and young adults. Use of twitter among adolescents is not as big as other social networks. The vast majority of teens nowadays have a cell phone. In the period between 2005-2010, ownership of a cell phone became common even among the youngest teens. 58% of 12-year-olds had a cell phone in 2010.

Internet use has become ever-present in teens and young adults alike. 93% of adolescents 12-17 go online, as do 93% of young adults aged 18-29. Teens use the internet for a wide variety of activities from reading local news and politics to researching about health topics [10].

Concerns have been raised over the use some adolescents make of social networking sites. A study performed in 2010 analyzing 2423 randomly sampled MySpace profiles that were accessed in 2006 showed that the vast majority of adolescents were making sensible choices with the information they shared publicly. A year later, in a follow-up study, the results obtained were very similar, with a few exceptions. This seems to suggest that youth are exercising caution with the personal information they reveal on the internet [11]. Bakker

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and De Vreese, in a study conducted in 2011, found that a range of Internet uses correlate positively with distinct forms of political participation, emphasizing the importance that new forms of media have on the way youths interact with the world around them [12]. Also, the Internet and the anonymity it provides can help people discuss about topics, such as drug use, that would normally be taboo due to the stigma associated with them [13].

3.2 Health behavior in adolescents

Health behaviors, as defined by Kasl and Cobb, are any activities undertaken by a person who believes himself to be healthy for the purpose of preventing disease or detecting disease in an asymptomatic stage [14]. Individual behaviors are a very important factor in determining the health of an individual. They determine about 40% of a person’s health. The remaining 60% is made up of genetics [30%], social and environmental factors [20%], and health care [10%] [15]. Now we will proceed to analyze different elements of adolescents’ health behaviors.

Skipping breakfast is a very common thing among adolescents today [16]. A study performed in Greece in 2011 where a group of 6500 boys and 7778 girls aged 13-19 years in which anthropometric measurements were taken showed than skipping breakfast was related to a constellation of bad habits that had an overall negative impact on health and an increase in BMI [17]. According to a HSBC study report from 2014, fruit intake seems to be increased among adolescent girls, those that hail from a low-income family and younger children [18]. This difference in gender could be due to the fact that females are usually more health aware than males and thus take more steps towards a healthy lifestyle. It also seems likely that family affluence plays an important role in the awareness female adolescents have of their bodies [19]. A study performed by Stok and others in 2014 showed that a descriptive message manipulation held the potential to change bad habits among adolescents by increasing their fruit intention consumption in the following days after the message was delivered. This means that rather than telling them what to do we should aim at pointing out what others do and the benefits associated with such actions [20].

Regarding soft drinks, it appears that boys consume them more often than their female counterparts, this consumption increasing as they grow older [18]. This consumption of soft drinks could be worrisome due to a possible association between high intake of these beverages and a low consumption of other more nutritional drinks such as milk and fruit juices [21]. Home environmental factors, habit strength, and intention seemed to have the

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biggest impact on consumption of soft drinks at the home environment. It is important to tackle this problem because soft drink consumption is an important element in reducing the risk factors for obesity in the youth [22]. Television’s junk food and soft drink advertising also seems to increase the chances that kids will consume more of these products. Exposure to adds advertising sugar-carbonated drinks led to an increase of 9.4% in consumption of such drinks by kids that were exposed to such adds during 2002-2004 in the year 2004 [23]. Lastly, a literature review conducted in 2012 explained the risks associated with the high availability of caffeine-containing energy drinks and the overall unawareness of adolescents about the possible risks for the health when multiple of these drinks are taken at once. Thus, physicians should be conscious of these risks and take measures appropriately [24].

Oral health is also another important aspect in the health behavior of adolescents. There is an established relationship between poor oral hygiene and an increased risk of a cardiovascular event and an increase in inflammatory markers [25]. Other studies have shown the relationship between poor oral hygiene and increased odds of developing metabolic syndrome [26]. Brushing of the teeth twice a day is the recommended method by odontologists to avoid the developing noncommunicable, periodontal diseases, and caries. Fortunately, there has been an increase in the frequency of toothbrushing in the span between 1994 and 2010, with differences between different European countries diminishing all throughout. Although the results are positive, they are still not good enough as in many countries they don’t reach the minimum of two toothbrushes a day, especially among boys [27]. A study in the UK showed that kids were not properly taught the importance of brushing their teeth and that on their minds not brushing them was related with fears such as their teeth falling out or issues of personal appearance rather than caries avoidance [28]. There also seems to be a strong association between smoking and infrequent toothbrushing [29].

Moving on to physical activity, its impact on health in adolescents is well established. Physical activity is linked with positive health outcomes, the more physical activity the better the outcome. It has been recommended that at least 60 minutes of physical activity per day be done, though some of the benefits can be reaped from 30 minutes [30]. A literature review done by Singh and others in 2012 found that physical activities were related with an improvement in academic performance in children [31]. Unfortunately, it appears that about 80% of adolescents worldwide don’t meet the minimum requirement of at least 60 minutes of moderate to vigorous physical activity per day [32,33].

Watching television is an important factor that contributes to the sedentary behavior in adolescents. They usually spend a big portion of their time watching TV, a habit that seems to follow them from childhood [34]. This sedentary behavior has catastrophic consequences for

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their health, as proven by various studies [35,36]. Current guidelines recommend that children cut their screen-based recreational time to less than 2 hours a day [37,38]. Time spent on TV seems to have decreased in the last 10 years, but this decline has been replaced by an increase in the time spent in new forms of media, such as computers, tablets, smartphones, and videogames. Thus, special emphasis should be put in these new forms of media [38].

Tobacco use is one of the first causes of preventable deaths in the world and is calculated to kill about six million people worldwide [39]. Adolescence is an important period when it comes to tobacco since it is at this age that initiation usually starts. Adverse effects to their health usually occur immediately, such as addiction, asthma, reduced lung capacity, and impaired lung growth [40]. Adolescents seem to be making use of new forms of tobacco, such as e-cigarettes and hookahs, and seem to be leaving aside more traditional forms of use, such as cigarettes and cigars. This change in trend has not significantly altered the total consumption of tobacco though [41]. An important factor in initiating adolescents into smoking seems to be whether or not one or both parents smoked in front of the child, increasing the likelihood that the child will smoke in the future significantly. The same effect happens when a sibling or some other family member exposes them to smoking. Thus, this is a risk factor that can be completely avoidable if the kid is protected from exposure [42].

Alcohol consumption is one of the most common forms of drug use among adolescents [43,44]. Consumption may be related to the role alcohol plays in the consolidation and formation of new and stronger ties among adolescents [45]. Alcohol consumption is associated with many different short- and long-term outcomes, such as risky sexual behavior, accidents, sexually transmitted infections, and pregnancy, sexual assault and violence victimization, obesity, use of other substances. It may also affect academic performance and have negative social and familial outcomes [46]. According to some research, it appears that alcohol consumption may also have an effect in the brain development process, but further investigation regarding this is necessary to arrive at some definitive conclusion [47]. More certain is the fact that those that engage in noxious habits show poorer neurocognitive performance, changes in gray and white matter brain structure, and discordant functional brain activation compared to those teens who don’t use alcohol [48]. Rates of alcohol use seem to be decreasing in Europe and North America since the beginning of the 21st century [49]. However, there is still a significant increase in the rates of alcohol use the older the teen grows [especially between 13 and 15] [18]. Although this is still more common among males, gender differences seem to be decreasing according to literature [18,50,51]. The socioeconomic status of teens doesn’t seem to have any influence on their overall consumption of alcohol [18,52]. More important than family affluence are factors such

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as family, community support, a stable and cohesive family unit, and perceived social position among peers in preventing risky habit-forming and transitioning healthily into adulthood [53].

When it comes to cannabis, it is one of the most used drug among adolescents, with about 14.6 million users each year [54]. Cannabis is considered to be a “gateway” drug, meaning that its consumption seems to be necessary in order for stronger and more dangerous drugs to be consumed [55]. The reasons behind the use of cannabis are experimentation, boredom, enjoyment, social enhancement, conformity and relaxation. Those who use it for experimentation usually use it less and experience fewer problems than those who use for enjoyment, activity enhancement, and habit [56]. Although the popular perception is that marijuana is a harmless substance that shouldn’t even be banned, the research seems to suggest otherwise. Marijuana use, like any other drug, is associated with the risk of becoming addicted to it, with approximately 9% of those who ever consume it becoming addicts. There is also the risk of affecting brain development, with those who consume it regularly having impaired neural connectivity in certain brain zones. Also, depression and anxiety seem to be more common among marijuana users [57]. There seems to be a positive relationship between the amount of evenings spent with friends and cannabis use. More research is required to find out why this is the case [58]. Other factors that seem to influence cannabis use are whether or not there is a member in that family that consumes it [59] and patterns of parenting, either too little or too much discipline-oriented parenting [60].

Lastly, we will tackle cyberbullying. Although cyberbullying is less commonplace than standard bullying, both types have similar effects on those being victimized. The most common types of cyberbullying are phone calling and text messaging, followed by instant messaging. Bullying by mobile phone or videos seems to have more of a negative impact. Also, being a victim of cyberbullying appears to be linked with internet use [61]. There are 7 subtypes of cyberbullying that have been identified: text message bullying; picture/video clip bullying [through mobile phone cameras]; phone call bullying; email bullying; chat-room bullying; bullying through instant messaging; and bullying through websites [62]. Bullying and cyberbullying should be taken seriously due to its association with suicidal ideation, attempted suicides, and depression. Specifically, more emphasis should be put into identifying adolescents suffering from depression that are being bullied, due to the greater risk they have of suicide [63,64]. Lastly, cyberbullying is also associated with negative outcomes when it comes to academic performance, violence, bad relations with peers, unsafe sex and substance abuse [65-68].

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15 3.3 Studies related to our topic

Mathers and others, in a cross-sectional community study described that adolescents spent on average 3 hours and 16 minutes a day making use of electronic media, with television on top with an average of 128 minutes. Next was videogames with 35 minutes, followed by computers and telephone, with 19 and 13 minutes respectively. They also reported that big amounts of electronic media use were associated with poor health outcomes. According to their study, spending time at the computer typing or just browsing the internet was positively linked with psychological distress. On the other hand, playing videogames was associated with poorer health outcomes, such as anxiety/depression [69].

Best and others, in a systematic review of the literature found that possible benefits of IT are high self-esteem, perceived social support, increased social capital, feeling of safety while experimenting with their identity and increased chances for self-disclosure. Possible harmful effects discovered were increased exposure to harm, social isolation, depression and cyber-bullying. The majority of the studies they analyzed showed no significant impact of adolescent’s health. Thus, they argue that due to the contradictory nature of the literature, further research is required in this field to clarify the relationship between IT use and health outcomes in teens [70].

Another study by Strasburger and others reports that kids spend from 7 to 11 hours in different kinds of media and that this media can be significantly used to alter their behavior. They also agree that these media can have several effects on their health, such as the development of obesity, aggressive behavior, substance use, early sexual activity, and eating disorders. Thus, they argue that doctors, parents and schools should adapt to these new technologies and understand the impact they have on the kids’ health [38]. Also on this theme of intervention, Hieftje and others also agreed that these new tools of communication could be used effectively to change health attitudes in adolescents [71].

Militello and others suggested using text messaging as a way to bridge the gap in health disparities between different social sectors. They argued that text messaging can help by acting as a reminder to encourage positive changes in lifestyle [72]. Wang and others, in their study, argued that the outcomes of using IT depended upon the usage done of said technologies. For instance, when the internet was used to search for health-related information or obtaining knowledge it was linked with a positive health outcome. Other habits of Internet use, however, were associated with negative lifestyle behaviors [73]. Following this theme, Fossum and others found that use of the computer and mobile phone while trying to fall asleep was associated with insomnia, and negatively linked with morningness [74]. A

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different study by Tavernier and Teena in an adult population clarified that people used the internet and television as a means of coping with their insomnia. It doesn’t seem like use of these media types causes insomnia or is related with sleep problems at all [75].

Regarding cyberbullying, Bottino and others found that daily use of 3 or more hours of Internet, web camera, text messages, sharing personal information and harassing others online were associated with cyberbullying. Range of cyberbullying in their study ranged between 6.5% to 35.4%. There also seems to be a relationship between cyberbullying and depression, substance use, ideation and suicide attempts [76]. In relation to substance abuse, Fisoun and others found that a pathological use of the internet increases the chances that the user has consumed an illegal substance. From this, they conclude that internet addiction could be used as a predictor for substance consumption experiences [5]. From all of this body of research we can conclude that since all of this is still a nascent field of investigation, more studies are required that study the cross-national tendencies and impact of IT on the adolescents’ patterns of behaviors and lifestyles.

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4. SUBJECTS AND METHODS

4.1 Subjects and study design

The data presented here were obtained from the Health Behaviour in School-aged Children (HBSC) study, a cross-national survey, which was completed in 2013/2014 with the help of the World Health Organization (WHO, Europe) in 42 countries. More detailed background information about the study is provided in its international report [Inchley et al. 2016] and website: www.hbsc.org (HBSC 2017). For our study, data from all the countries in the report were analyzed.

Data were collected in all participating countries through school-based surveys using a standard methodology detailed in the HBSC 2013/2014 international study protocol [77]. Data collection methods ensured that the sample was representative. Around 1500 students in each HBSC country or region were selected from each age group in the 2013/2014 survey, making up about 220 000 in total. Previous analyses of HBSC indicate that a sample size of 1500 will ensure a 95% confidence interval. In practice, a lot of countries chose a sample size bigger than 1500, in order to increase the precision of estimates in subpopulations. A census survey approach was considered appropriate in Greenland, Iceland, Luxembourg and Malta due to the small populations of young people in these countries.

Out of the 44 countries registered for HBSC, 42 completed the 2013/2014 survey and met the guidelines for publication in the report. Those not included were unable to conduct the survey. Fieldwork took place mainly between September 2013 and June 2014, except for 4 countries, where an extended fieldwork period was required to reach the proper sample size.

The data were collected by means of self-report standardized questionnaires. The surveys were administered in school classrooms ensuring students’ confidentiality. Response rates at the school averaged over 60% in most countries and regions. The mean ages across the whole sample were 11.6, 13.5 and 15.5 years. In table 1 a more detailed display of our sample is shown.

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18 Table 1. Sample size displayed by countries and in total

Countries Sample size (N)

Albania 5024 Armenia 3679 Austria 3458 Belgium (French) 5892 Belgium (Flemish) 4393 Bulgaria 4796 Canada 12931 Switzerland 6634 Czech Republic 5082 Germany 5961 Denmark 3891 Estonia 4057 England 5335 Spain 11136 Finland 5925 France 5691 Greenland 1020 Greece 4141 Croatia 5741 Hungary 3935 Ireland 4098 Israel 6193 Iceland 10602 Italy 4072 Lithuania 5730 Luxembourg 3318 Latvia 5557 Republic of Moldova 4648 Macedonia 4218 Malta 2265 Netherlands 4301 Norway 3072 Poland 4545 Portugal 4989 Romania 3980 Russian Federation 4716 Scotland 5932 Sweden 7700 Slovenia 4997 Slovakia 6099 Ukraine 4552 Wales 5154 Total 219,460

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19 4.2 Ethics

The study conformed to the principles outlined in the Declaration of Helsinki. National and local educational institutions agreed upon the study protocol. Ethical approval was obtained for each national survey according to the national guidance and regulations at the time of data collection. Researchers strictly followed the standardized international research protocol to ensure consistency in survey instruments, data collection and processing procedures [HBSC 2013].

4.3 Measures

The variables used were IT, a new variable created by us from 5 previous ones. In addition to IT, we also used 3 other variables related with the time adolescents spend a week in watching TV, playing computer games, and using the computer. Variables related to addiction (smoking, alcohol, and cannabis) and cyberbullying were also used. Analyses were controlled for gender and age.

IT. In order to create this variable, we combined the following default variables from

the study: m90, talk to friends phone/internet; m91, using texting/sms; m92, using email; m93, using instant messaging; m94, other social media. In all of these variables, subjects were asked how often they used the abovementioned media, to which they had 4 choices: (1) Hardly ever or never; (2) Less than weekly; (3) Weekly; (4) Daily, how often during a day? _______ times. We combined all of these into 1 in the following way: we took each of the numbered responses as a numerical value and added them up for the 5 variables (e.g., 1 for m90, 1 for m91, 1 for m92, 1 for m93, 1 for m94; 1+1+1+1+1=5). The minimum value possible is 5 and the maximum 20. Then, in order to create categories for each of these values we applied the following formula: X-5=Y with X being the result obtained from the process described above and Y being the resultant category we have just created. Thus, we will have 16 categories in total ranging from 0 to 15, depending on how much combined use of the 5 original categories the subject makes. With 0 being no use at all, and 15 being very extensive use.

Total time a week spent watching TV, playing computer games, using computer.

Participants were asked how much time a day they spent watching TV/playing computer games/using computer on weekdays and on the weekend. Possible answers were: (1) None at all; (2) Half an hour a day; (3) 1 hour a day; (4) 2 hours a day; (5) 3 hours a day; (6) 4 hours a

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day; (7) 5 hours a day; (8) 6 hours a day; (9) 7 hours or more a day. For the purposes of our study, we combined the weekdays and weekend hours per day into a single category for each variable and applied the following formula to estimate the total amount of time per week subjects used the 3 abovementioned variables: Total hours a week = 5 × Hours a day on

weekdays + 2 × Hours a day on weekends. This way, we got three different variables

describing the estimated total number of hours per week subjects spend watching TV, playing computer games, and using the computer.

Smoking. Subjects were asked: How often do you smoke tobacco at present? Possible

answers were: (1) Every day; (2) At least once a week, but not every day; (3) Less than once a week; (4) I do not smoke. For the purposes of our study, we grouped them into smokers and nonsmokers. For that we took 1, 2, and 3 as smokers and 4 as nonsmokers and combined them into a new variable.

Alcohol. For the purposes of our study, we took drunkenness in lifetime as the

variable of interest. Subjects were asked: Have you ever had so much alcohol that you were

really drunk? Possible answers were: (1) No, never; (2) Yes, once; (3) Yes, 2-3 times; (4)

Yes, 4-10 times; (5) Yes, more than 10 times. We created a new variable by considering (1) and (2) as 0 = never or 1 time; and (3), (4), and (5) as 1= 2 or more times.

Cannabis. Subjects were asked: Have you ever taken cannabis (insert appropriate street names here) ...? Possible answers were: (1) Never; (2) 1-2 days; (3) 3-5 days; (4)

6-9 days; (5) 10-19 days; (6) 20-29 days; (7) 30 days or more. For our study, we simplified this variable and created a new one with just 2 groups: 0 = never (includes only (1) from original variable); and 1 = at least once (uses (2) through (7) from original variable).

Bullying and cyberbullying. Bullying has been defined as negative physical or verbal

actions that have hostile intent, cause distress to victims, are repeated over time, and involve a power differential between bullies and their victims. The subject was introduced as follows: "Here are some questions about bullying. We say a student is being bullied when another

student, or a group of students, say or do nasty and unpleasant things to him or her. It is also bullying when a student is teased repeatedly in a way he or she does not like or when he or she is deliberately left out of things. But it is not bullying when two students of about the same strength or power argue or fight. It is also not bullying when a student is teased in a friendly and playful way." Then, the students were asked: How often have you been bullied in the past couple of months in the following ways: (a) Someone sent mean instant messages, wall postings, emails and text messages, or created a website that made fun of me; (b) Someone took unflattering or inappropriate pictures of me without permission and posted them online?

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(2) Only once or twice; (3) 2 or 3 times a month; (4) About once a week; (5) Several times a week. We simplified and dichotomized this variable as follows: 0 = no cyberbullying (includes only (1) from original variable); and 1 = any cyberbullying (includes (2) through (5) from original variable).

4.4 Statistical analysis

Statistical analyses were conducted on 219460 subjects with no missing values for any of the IT use, risk behaviors, and cyberbullying variables studied. The analyses were stratified by countries. In addition, the analyses were conducted on the data of all 42 countries.

Data were analyzed using SPSS software (version 20; SPSS Inc. Chicago, IL, 2011). To summarize the characteristics of our study sample, we produced descriptive statistics (frequencies, percentages, mean, standard errors) including all the demographic, independent and dependent variables of interest. To evaluate potential differences between respondents’ groups in all categorical variables, we applied a z-test (for binary variables) or Chi-square test (for remaining variables). To test the association between IT use, total hours spent watching TV, playing computer games, using the computer, and risk factors such as tobacco use, alcohol consumption, cannabis use, and cyberbullying, we conducted a series of univariate and multivariate binary logistic regression analysis, with total hours spent watching TV, playing computer games, using computer, and risk factors such as tobacco, alcohol, cannabis consumption, and cyberbullying as dependent (outcome) variables, and IT use as a binary predictor (independent variable). Associations were estimated using odds ratios (OR) with 95% confidence intervals (95% CI), which indicated the relationship between hours spent at TV, computer games, and computer; the likelihood of consuming the substances abovementioned and being cyberbullied for persons with certain characteristics (IT use) relative to the reference group (little or no use of IT). In multivariate logistic regressions, associations were adjusted (controlled) for the effect of possible confounders (gender and age). We used Enter method with all independent variables irrespective of their significance found in a univariate analysis. P<0.05 was considered statistically significant.

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5. RESULTS

5.1 Analysis of influence of gender and age in IT use

Our results show that gender and age have a significant impact in the use of IT. In almost all cases, the relationship was very significant (P <.001) for both gender and age. Only in 5 countries (Armenia, Switzerland, Finland, France, and Italy) did gender not have a significant impact on IT use. From our results regarding gender we can state that girls make a bigger use of IT technologies than boys do. Only in Albania were boys found to use IT more than girls.

Similar to gender, age was also found to be very significant (P <.001) in almost all countries studied. Only in 6 countries (Austria, Switzerland, England, Finland, France, and Israel) did age not make a difference in use of IT. The results of our analysis showed that the older the subjects are, the more use of IT they will make. Thus, 15-year-olds were found to make more use of IT than their 13- and 11-year-olds counterparts. Table 2 shows our results in detail.

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23 Table 2. IT relationship with sedentary behaviors, gender, and age: results from

linear regression analysis

Country TV Computer

games

Computer Gender Age 1 dummy (1311) Age 2 dummy (1511) Albania -.017 *** .049*** .141*** -1.910*** _{1.557 ***} _{2.645 ***} Armenia -.013 .063*** .161*** .209 1.217*** 1.581*** Austria .009 .020*** .037*** .397*** - -.268 Belgium (French) -.031*** .025*** .083*** .297*** - .615*** Belgium (Flemish) -.005 .021*** .074*** .756*** 2.113*** 2.802*** Bulgaria .010*** .014*** .056 *** .871 *** .774 *** 1.080 *** Canada -.019 *** .020 *** .065 *** .987 *** .988 *** 1.430 *** Switzerland -.012 .036 *** .060 *** .008 - .047 Czech Republic -.005 .020 *** .040 *** .809 *** 1.215 *** 1.689 *** Germany .001 .042 *** .051 *** .204*** 1.483 *** 1.659 *** Denmark -.008 .009 .042 *** .503 *** 1.568 *** 2.354 *** Estonia .010*** .018 *** .057 *** .951 *** 1.054 *** 1.460 *** England -.033 *** .027 *** .051 *** .392*** - .072 Finland -.017*** .001 .042 *** -.124 .437 .274 France -.013 .008 .069 *** .149 - 1.518 Greece .004 .020*** .088 *** .474 *** 2.496 *** 3.130 *** Croatia -.004 .024 *** .064 *** .847 *** 1.560 *** 2.104 *** Hungary .005 .041 *** .060 *** .825 *** 1.415 *** 2.247 *** Ireland -.040 *** .030 *** .082 *** .732 *** 1.994 *** 2.252 *** Israel -.002 .026 *** .058 *** .552 *** -.027 -.087 Iceland -.029 *** .009*** .063 *** .835 *** 1.817 *** 2.642 *** Italy -.027 *** .044 *** .075 *** -.008 1.294 *** 1.491 *** Lithuania -.007 .016*** .079 *** .594 *** .551 *** .733 *** Luxembourg .001 .040 *** .066 *** .716 *** 1.875 *** 2.522 *** Latvia -.004 .013*** .070 *** 1.343 *** 1.416 *** 1.724 *** Republic of Moldova .001 .025 *** .076 *** .520 *** .870 *** 1.771 *** MKD .001 .018*** .072 *** .361*** .955 *** 1.876 *** Malta .001 .042 *** .050 *** .285*** 1.695 *** 2.642 *** Netherlands -.014*** .026 *** .056 *** .611 *** 1.577 *** 1.837 *** Norway -.031*** .017 .040 *** .813*** 2.158 *** 2.432 *** Poland -.012*** .005 .090 *** .964 *** 1.444 *** 1.836 *** Portugal -.011*** -.004 .095 *** .781 *** 1.461 *** 2.696 *** Romania .007 .019*** .072 *** .720 *** 1.577 *** 2.284 *** Russian Federation -.010 .009 .072 *** .858 *** .808 *** 1.232 *** Scotland -.037 *** .027 *** .063 *** .459 *** 1.327 *** 1.468 *** Sweden -.018 *** .027 *** .045 *** .526 *** 1.041 *** 1.354 *** Slovenia .010 .020*** .086 *** 1.133 *** 1.651 *** 2.414 *** Wales -.014*** .030 *** .060 *** .802 *** 1.068 *** 1.365 *** The first value represents the odds ratio in a logistic regression analysis. Asterisks (***) represent significance (P<0.05). Age 1 dummy compares 13- to 11-year-olds. Age 2 dummy compares 15- to 11-year-olds.

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24 5.2 Relationship between IT and sedentary lifestyle

For our study, we focused on estimating the total IT use for different countries and in total. From that, we inferred that the bigger the use of IT, the more sedentary lifestyles the subjects will have. We also used 3 parameters that we defined as sedentary behaviors (TV, computer game, computer use) and analyzed their relation with IT. In order to be able to analyze the results we got from the IT variable we described before, we divided our results into two groups, one group made up from categories 0 to 7, labelled as “nonextensive use”, and the other group made from 8 to 15, labelled as “extensive use”. With this mind, we found that, in total, with all the countries taken together as a single unit, the difference between the two categories was insignificant (48.3% fell under nonextensive use, while 51.7% fell under extensive use). Thus, globally, we can’t say there is a tendency towards neither extensive nor nonextensive use. Having said that, when we analyze countries individually, we found some stark contrasts in some of them. For instance, in countries such as Finland, Austria, England, and Macedonia, the percentage of subjects in the extensive use group was around 70%, indicating a clear tendency towards heavy IT use and thus a greater likelihood of a sedentary lifestyle. Yet in others, such as Czech Republic and Portugal, the percentage of subjects in the nonextensive use group was much greater than the other one (75.1% and 64.2% respectively), indicating a greater likelihood of a less sedentary lifestyle. Table 3 shows our results in detail.

Regarding the relationships between our defined sedentary parameters and IT, our results showed that computer use and playing computer games had a very significant (P<.001) and positive relationship with IT. Watching TV had a somewhat unclear relationship with IT. The relationship was significant in less than half the countries studied, and in those in which it showed significance the relationship was negative most of the time. Computer use had the biggest impact out of three parameters, followed by computer games. Results are displayed on table 2.

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25 Table 3. Distribution of IT use by countries and in total

Countries %

Nonextensive use (from 0 to 7) Extensive use (from 8 to 15) Albania 53.8 46.2 Armenia 54 46 Austria 29.5 70.5 Belgium (French) 39.9 60.1 Belgium (Flemish) 45.1 54.9 Bulgaria 37.7 62.3 Canada 45 55 Switzerland 43.4 56.6 Czech Republic 75.1 24.9 Germany 58.6 41.4 Denmark 45 55 Estonia 47.5 52.5 England 33.3 66.7 Finland 26.8 73.2 France 33.9 66.1 Greece 50.7 49.3 Croatia 47.2 52.8 Hungary 48.5 51.5 Ireland 41.4 58.6 Israel 50.3 49.7 Iceland 55.1 44.9 Italy 50.3 49.7 Lithuania 40.4 59.6 Luxembourg 44 56 Latvia 42.2 57.8 Republic of Moldova 62.8 37.2 MKD 33.5 66.5 Malta 50.1 49.9 Netherlands 59.8 60.2 Norway 49.6 50.4 Poland 47.9 52.1 Portugal 64.2 35.8 Romania 49.9 50.1 Russian Federation 36.2 64.8 Scotland 45.1 54.9 Sweden 51.1 48.9 Slovenia 46.7 53.3 Ukraine 61 39 Wales 41.7 58.3 Total 48.3 51.7

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26 5.3 Relationship between IT use, addiction, and cyberbullying

Our results showed that use of IT is a good predictor for engagement in risk factors such as smoking, drinking alcohol, and cannabis use. More specifically, IT use is associated with, on average, twice the risk of tobacco use. In some countries, the risk increased up to three times (Luxembourg, Belgium (Flemish)). There were a few countries in which no significant relationships were found (Armenia, Bulgaria, Republic of Moldova).

Regarding alcohol consumption, the chances of drinking were twice as high the more IT was used. In some countries, this probability was thrice as high (Luxembourg, Belgium (Flemish)). Only in Israel was there no significant correlation found.

Likelihood of cannabis use was also increased to almost twice on average in relation to IT use. Most countries had a significant association between cannabis use and IT. In 9 countries, however, there was no significant relationship.

Cyberbullying is the variable that shows a less clear-cut relationship with IT use. For starters, only in 21 out of 39 countries was there a significant relationship. Of those, 19 show a positive coefficient averaging 1.85 and 2 (Bulgaria and Israel) show a negative coefficient. Thus, in almost half the countries there is a significant positive relationship between IT and cyberbullying, while in 2 countries there is a significant negative relationship. In the remainder of countries, no associations were found. Table 4 displays the results just discussed.

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27 Table 4. IT relationship to risk behaviors (smoking, alcohol, cannabis, and cyberbullying)

adjusted for gender and age: results from multivariate logistic regression analysis

Country Smoking Drinking Cannabis* Cyberbullying

Albania 2.81*** (1.98-3.97) 2.62*** (2.01-3.42) 2.53*** (1.39-4.52) 1.28 (0.92-1.78) Armenia 0.71 (0.43-1.16) 1.79*** (1.35-2.38) 3.21 (0.68-15.12) 0.76 (0.45-1.27) Austria 2.25*** (1.55-3.26) 2.25*** (1.55-3.29) 3.11*** (1.62-5.98) 1.14 (0.44-2.94) Belgium (French) 2.30*** (1.80-2.94) 2.24*** (1.77-2.84) 2.58*** (1.58-4.19) 1.84*** (1.24-2.73) Belgium (Flemish) 3.07*** (2.22-4.24) 3.10*** (2.3-4.17) 2.65*** (1.85-3.78) 2.70*** (1.67-4.38) Bulgaria 1.04 (0.85-1.26) 1.57*** (1.30-1.90) 1.40*** (1.06-1.85) 0.71*** (0.57-0.90) Canada 1.96*** (1.64-2.35) 2.87*** (2.44-3.36) 2.21*** (1.88-2.60) 2.26*** (1.87-2.73) Switzerland 1.81*** (1.41-2.34) 1.85*** (1.41-2.41) 1.83*** (1.47-2.27) - Czech Republic 2.33*** (1.86-2.92) 2.18*** (1.77-2.67) 1.86*** (1.44-2.41) 0.98 (0.60-1.59) Germany 1.93*** (1.60-2.33) 2.11*** (1.74-2.55) 1.98*** (1.54-2.55) 1.86*** (1.28-2.71) Denmark 2.00*** (1.46-2.74) 2.45*** (1.87-3.19) 2.42*** (1.58-3.72) 2.63*** (1.68-4.10) Estonia 2.18*** (1.66-2.87) 2.38*** (1.88-3.02) 1.99*** (1.48-2.68) 1.56*** (1.16-2.09) England 2.39*** (1.57-3.64) 2.02*** (1.61-2.54) 2.88*** (2.04-4.07) 2.28*** (1.26-4.12) Finland 2.45*** (1.88-3.19) 2.02*** (1.61-2.54) 1.17 (0.81-1.69) 1.10 (0.72-1.66) France 2.34*** (1.81-3.03) 2.02*** (1.51-2.70) 2.05*** (1.60-2.63) 2.87*** (1.10-7.48) Greece 1.74*** (1.31-2.31) 2.43*** (1.84-3.20) - 1.75 (0.98-3.14) Croatia 1.86*** (1.50-2.30) 2.02*** (1.66-2.47) 1.39*** (1.01-1.91) 1.35 (0.99-1.84) Hungary 3.07*** (2.34-4.03) 2.80*** (2.20-3.57) 2.00*** (1.25-3.18) 2.20*** (1.46-3.31) Ireland 1.72*** (1.23-2.41) 2.07*** (1.47-2.89) 1.43 (0.95-2.16) 1.68*** (1.20-2.36) Israel 0.76*** (0.61-0.94) 1.29 (0.95-1.76) 1.25 (0.76-2.06) 0.7*** (0.54-0.90) Iceland 1.40*** (1.06-1.86) 1.62*** (1.20-2.18) 1.36 (0.98-1.87) 1.32*** (1.03-1.69) Italy 1.82*** (1.45-2.29) 1.89*** (1.38-2.55) 1.21 (0.90-1.63) 1.79*** (1.16-2.76)

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28 Lithuania 1.60*** (1.32-1.94) 2.04*** (1.70-2.44) 1.97*** (1.42-2.74) 1.20 (0.99-1.45) Luxembourg 3.04*** (2.16-4.27) 3.02*** (1.95-4.67) 2.23*** (1.46-3.41) 1.38 (0.88-2.16) Latvia 2.48*** (1.96-3.14) 2.66*** (2.14-3.30) 3.01*** (2.17-4.18) 1.51*** (1.19-1.90) Republic of Moldova 0.97 (0.73-1.30) 1.27*** (1.02-1.59) 1.48 (0.94-2.34) 0.85 (0.61-1.17) MKD 1.44*** (1.02-2.03) 2.59*** (1.54-4.37) 1.18 (0.54-2.58) 0.99 (0.68-1.43) Malta 1.86*** (1.24-2.80) 2.12*** (1.46-3.08) 2.33*** (1.25-4.35) 2.19*** (1.35-3.53) Netherlands 1.71*** (1.30-2.24) 2.02*** (1.50-2.73) 1.61*** (1.17-2.22) 1.75*** (1.13-2.70) Norway 1.99*** (1.17-3.36) 2.73*** (1.80-4.15) - 1.49 (0.93-2.38) Poland 2.12*** (1.71-2.63) 2.17*** (1.73-2.72) 2.23*** (1.62-3.07) 1.20 (0.88-1.66) Portugal 1.99*** (1.46-2.69) 2.16*** (1.57-2.96) 2.08*** (1.33-3.25) 0.87 (0.57-1.33) Romania 1.77*** (1.39-2.27) 1.53*** (1.17-2.01) 2.17*** (1.24-3.80) 1.04 (0.71-1.53) Russian Federation 1.26** (1.01-1.58) 2.56*** (1.81-3.63) 1.44 (0.82-2.53) 0.91 (0.73-1.13) Scotland 1.89*** (1.44-2.47) 2.75*** (2.22-3.40) 1.77*** (1.32-2.37) 1.84*** (1.43-2.37) Sweden 2.22*** (1.72-2.85) 2.52*** (2.01-3.16) 1.89*** (1.30-2.76) 1.74*** (1.24-2.43) Slovenia 2.96*** (2.18-4.03) 3.41*** (2.66-4.38) 2.00*** (1.46-2.73) 1.50*** (1.07-2.11) Ukraine 1.55*** (1.22-1.97) 1.36*** (1.09-1.70) 1.67*** (1.13-2.48) 1.14 (0.87-1.49) Wales 1.53*** (1.13-2.06) 2.77*** (2.20-3.50) 1.92*** (1.36-2.72) 1.94*** (1.40-2.69)

The first value represents the odds ratio in a logistic regression analysis. The asterisks (***) represent significance (P<0.05). In parentheses are the lower and upper ranges of the confidence interval of 95% for the odds ratio.

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6. DISCUSSION

Our results regarding the analysis of IT use by gender and age agree with the literature we have consulted. Adolescent girls of an older age group tend to use IT more often than boys. The older the adolescents the more autonomy they are given by their parents and thus the more they make use of IT. Family affluence also seems to play an important role in the use adolescents make of IT. Teenagers from high affluence families have more opportunities to get the tools (smartphones, tablets, laptops, etc.) with which to access IT more easily [78]. The finding that girls of the older age groups make a more extensive use of IT goes in line with the fact that this target group of adolescent girls tend to be more sedentary when compared to boys [36]. Some studies have argued that differences in gender use of IT could be due to well-established gender roles. The feminine role is seen as warm, supportive, nurturing, sensitive and empathic toward the feelings of others [79]. Thus, this could be one of the reasons so as to why girls make a more intensive IT use to establish connections with peers. In our opinion, this could very well be the main factor tipping the scale on the females’ favor when it comes to IT use. While males make a heavy use of IT as well, it can’t be compared with the constant use females make of their smartphones, be it texting, browsing social media, or calling. Pate and others in a literature review done in 2011 described how the older the teens are the more time they spend in sedentary activities, with an average of 7.5 hours a day for teens around 15 years of age. [80]. These results seem to agree with ours since in our study the older the teens are the more they make use of IT.

Our results show that IT has a significant correlation with sedentary behaviors such as computer use and playing computer games. With computer use being the most prominent out of the 2. Santaliestra-Pasías et al. argued that new technologies and electronic-based media communication have caused a decline in physical activity levels, and an increment in sedentary activities in children and adolescents [81]. This goes in line with another study conducted in Turkey that showed that problematic levels of Internet use [and thus increased levels of immobility and sedentarism], were associated with increased BMIs in the obesity range [82]. George and Elisavet’s study also agree with our results. In their study, Internet use was found to have a negative relationship with exercise and physical activity [83]. On the other hand, while it is true that IT seem to be correlated with decreased physical activity and an increase in sedentary behaviors, this doesn’t mean that they can’t be used to change such habits. Stoicescu and Stănescu suggest to use mobile phones to enhance physical education in children [77]. In our view, such measures are a step in the right direction in order to change

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this trend of sedentarism and physical inactivity among our youth. Since fighting to reduce the use of smartphones and other forms of electronic media communication is an uphill battle with no likely success in sight, we believe it is better to use the “enemy’s weapons” against himself. That way, we can take advantage of the heavy use of these media to try to change the health outcomes of adolescents and children for the better.

Yonker and others, in a systematic review of the literature spanning from January 1, 2002 and October 1, 2013, argued that most of the studies done on social media use to influence adolescents’ behavior had been limited in their methodologies and that further research was required in order to come up with new strategies to influence their lifestyles in a positive manner [84]. Our study has shown that use of IT is related with an increase in consumption of tobacco, alcohol, and cannabis. Thus, we agree that further studies should be conducted in this field to try to change this trend for the better. Also, Fisoun et al., in a cross-sectional study from 2012, reported how adolescents with “pathological” internet use have increased chances of engaging in illicit drug use [5]. This agrees wholly with our results that IT use has a clear correlation with substance use. Whether use of IT is the cause or the consequence of this increase remains to be established. Further research on this topic is necessary in order to establish causality. We believe that rather than it being just a simple correlation between IT time and substance abuse, it has more to do with the use one makes of such technologies and other external factors, such as familial and personal components, surrounding the individual. Mathers and others in a cross-sectional study similar in scope to ours found that a high use of overall EMC was related with worse health status, behavior, and health-related quality of life. They found a different outcome depending on what type of EMC was being used. For instance, when used for typing/Internet there was a positive correlation with distress. On the other hand, when used for gaming there was a correlation with a poorer health status. On their study, back in 2009, television was on average the most used form of entertainment [69]. Nowadays television has been surpassed by other forms of entertainment such as the internet, use of mobile phones and social networks. Our study shows that computer use, followed by computer games, has the greatest correlation with IT use. TV, on the other hand, seems to have fallen by the wayside. This seems to suggest a change in the way adolescents entertain themselves and may be explained by the rapid change in technology this new generation has experienced. Our results are in agreement when it comes to the association between high use of EMC and poorer health outcomes.

Regarding cyberbullying, our results were equivocal. In 19 countries, there seemed to be a correlation between electronic media communication use and cyberbullying. The chances of being cyberbullied were almost twice as high the more IT were used. However, if the

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remainder of countries there was no correlation or a negative one was present. This seems to indicate that the problem with cyberbullying may have more confounders affecting the outcome. Bottino et al., in a systematic review of the literature found that people that were bullied in “traditional” ways were also cyberbullied. They also reported that both victims and aggressors had emotional and psychosomatic problems [76]. This seems to suggest that cyberbullying, just as bullying, is a complex phenomenon with an interplay of different factors ranging from personal characteristics of both the victim and the aggressor, the social and personal environments, and IT use. Further research in this topic is required in order to assess what factors play the most prominent roles in this phenomenon so that appropriate measures can be taken to curtail its prevalence.

Our study revealed certain differences between countries in all the aspects studied. The relevance and significance of these differences are up for debate. We are certain that part of these differences can be attributed to errors and limitations in the data collection process and in the way the study was conducted. Leaving that aside, it is difficult to establish for certain what particular differences between countries may mean, let alone establish causality. We suggest that further studies be conducted in the countries in which cross-national trends appear not to take place so that further insight can be obtained as to why this happens. This way, we will gain invaluable knowledge of the idiosyncrasies of the countries that lead up to the results obtained in the hopes of applying such information to the betterment of the health outcomes of the youth globally. Just to give an example, we could put forth the case of Albania. This is the only country in which males make more use of IT than females. When compared to other countries, Albanese boys make a more extensive use of IT than most females in other countries. It would be interesting to conduct research about this particular phenomenon and find out the reasons as to why this happens just in Albania and not elsewhere. Results from such studies could shed some light into the motives for such a deviation from cross-national trends.

Lastly, we will address the limitations of our study. Data obtained by surveys often have the problems of reliability and validity. Reliability deals with the issue of obtaining reproducible answers every time the survey is conducted. Validity deals with obtaining the information we were looking for with the survey. Another limitation is that in survey-based studies the establishment of causal relationships between two variables is hard to prove. However, even with all these limitations, pilot studies have proven that we must still believe in the data obtained by such methods because they are still reliable and provide us with useful insight into relationships between people, places, and things as they exist in the real world.

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They also allow for data collection about what people think and feel, and ease collecting data in greater breadth and depth.

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7. CONCLUSIONS

1. Age and gender play a prominent role in the quantitative use adolescents make of Internet

Technologies (IT). In general, girls tend to make a more extensive use of IT than boys. In regards to age, the older the adolescents are, the more intense use of IT they will make.

2. IT use and computer usage have a positive association. Generally, the more IT are used, the

greater the use of computer and computer games, which involve a bigger degree of sedentarism and lack of physical activity.

3. A positive correlation was found between IT use and tobacco, alcohol, and cannabis

consumption. The greater the use of IT, the bigger the chance of noxious substance use.

4. Relationships between IT use and cyberbullying were less clear. In our study, in about half

the countries there was a positive correlation between IT and cyberbullying. The other half showed no correlation whatsoever. This seems to indicate that there are other factors that influence cyberbullying aside from IT use, pointing towards a complex phenomenon that should be further studied.

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8. PRACTICAL RECOMMENDATIONS

We recommend, as highlighted by other researchers, to make use of EMCs to influence adolescents’ behaviors and teach them the importance of a healthy, physically active, and drug-free lifestyle. We believe that this may be the most efficient method to affect their behavior in a significant way. Special emphasis should be put on girls of the older age groups, since they are the group that, as they age, spend less time in physical activities. So, physical programs and activities especially tailored for teenage girls should be devised to encourage the promotion of a more physically active lifestyle. Schools should also work closely with governmental agencies to develop awareness programs into not just cyberbullying but also “plain” bullying, since the 2 are closely related and follow one another. Teachers and school peers should be on the lookout for any signs of abuse or bullying and report it immediately, as well as trying to stop it by confronting the bully and giving support to the victim.

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REFERENCES

[1] Jones S, Fox S. Generations Online in 2009. 2009.

[2] Greenfield P, Yan Z. Children, adolescents, and the Internet: A new field of inquiry in developmental psychology. Dev Psychol 2006;42(3):391-394.

[3] Lam LT, Peng Z, Mai J, Jing J. Factors Associated with Internet Addiction among Adolescents. CyberPsychology & Behavior 2009 10/01; 2017/03;12(5):551-555.

[4] Stahl C, Fritz N. Internet safety: adolescents’ self-report. Journal of Adolescent Health 2002 7;31(1):7-10.

[5] Fisoun V, Floros G, Siomos K, Geroukalis D, Navridis K. Internet Addiction as an Important Predictor in Early Detection of Adolescent Drug Use Experienceâ€”Implications for Research and Practice. Journal of Addiction Medicine 2012;6(1).

[6] Side Effects of Internet Addiction: Sedentary & Solitary. 2014; Available at:

http://internetaddictionresource.com/2014/02/28/side-effects-of-internet-addiction-sedentary-solitary/. Accessed 03/08, 2017.

[7] Roberts DF, Foehr UG. Trends in media use. The future of children 2008;18(1):11-37.

[8] Kietzmann JH, Hermkens K, McCarthy IP, Silvestre BS. Social media? Get serious! Understanding the functional building blocks of social media. Bus Horiz 2011;54(3):241-251.

[9] O'Keeffe GS, Clarke-Pearson K, Council on Communications and Media. The impact of social media on children, adolescents, and families. Pediatrics 2011 Apr;127(4):800-804.

[10] Lenhart A, Purcell K, Smith A, Zickuhr K. Social media and young adults. Pew Internet & American Life Project 2010;3.

[11] Patchin JW, Hinduja S. Trends in online social networking: Adolescent use of MySpace over time. new media & society 2010.

[12] Bakker TP, De Vreese CH. Good news for the future? Young people, Internet use, and political participation. Communication research 2011;38(4):451-470.

[13] Discussing illicit drugs in public internet forums: Visibility, stigma, and pseudonymity. Proceedings of the 5th International Conference on Communities and Technologies: ACM; 2011.

[14] Kasl SV, Cobb S. Health behavior, illness behavior and sick role behavior: I. Health and illness behavior. Archives of Environmental Health: An International Journal 1966;12(2):246-266.

[15] Schroeder SA. We can do better—improving the health of the American people. N Engl J Med 2007;357(12):1221-1228.

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36

[17] Kapantais E, Chala E, Kaklamanou D, Lanaras L, Kaklamanou M, Tzotzas T. Breakfast skipping and its relation to BMI and health-compromising behaviours among Greek adolescents. Public Health Nutr 2011;14(01):101-108.

[18] Inchley J, Currie D. Growing up unequal: gender and socioeconomic differences in young people’s health and well-being. Health Behaviour in School-aged Children (HBSC) study: international report from the 2013;2014.

[19] Wardle J, Robb KA, Johnson F, Griffith J, Brunner E, Power C, et al. Socioeconomic variation in attitudes to eating and weight in female adolescents. Health Psychology 2004;23(3):275.

[20] Stok FM, Ridder DT, Vet E, Wit JB. Don't tell me what I should do, but what others do: The influence of descriptive and injunctive peer norms on fruit consumption in adolescents. British journal of health psychology 2014;19(1):52-64.

[21] Harnack L, Stang J, Story M. Soft drink consumption among US children and adolescents: nutritional consequences. J Am Diet Assoc 1999;99(4):436-441.

[22] Tak N, Te Velde S, Oenema A, Van der Horst K, Timperio A, Crawford D, et al. The association between home environmental variables and soft drink consumption among adolescents. Exploration of mediation by individual cognitions and habit strength. Appetite 2011;56(2):503-510.

[23] Andreyeva T, Kelly IR, Harris JL. Exposure to food advertising on television: associations with children's fast food and soft drink consumption and obesity. Economics & Human Biology

2011;9(3):221-233.

[24] Rath M. Energy drinks: what is all the hype? The dangers of energy drink consumption. Journal of the American Association of Nurse Practitioners 2012;24(2):70-76.

[25] de Oliveira C, Watt R, Hamer M. Toothbrushing, inflammation, and risk of cardiovascular disease: results from Scottish Health Survey. BMJ 2010 May 27;340:c2451.

[26] Kobayashi Y, Niu K, Guan L, Momma H, Guo H, Cui Y, et al. Oral health behavior and metabolic syndrome and its components in adults. J Dent Res 2012;91(5):479-484.

[27] Honkala S, Vereecken C, Niclasen B, Honkala E. Trends in toothbrushing in 20 countries/regions from 1994 to 2010. Eur J Public Health 2015 Apr;25 Suppl 2:20-23.

[28] Gill P, Stewart K, Chetcuti D, Chestnutt I. Children’s understanding of and motivations for toothbrushing: a qualitative study. International journal of dental hygiene 2011;9(1):79-86.

[29] Honkala S, Honkala E, Newton T, Rimpelä A. Toothbrushing and smoking among adolescents– aggregation of health damaging behaviours. J Clin Periodontol 2011;38(5):442-448.

[30] Janssen I, LeBlanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral nutrition and physical activity 2010;7(1):40.

[31] Singh A, Uijtdewilligen L, Twisk JW, Van Mechelen W, Chinapaw MJ. Physical activity and performance at school: a systematic review of the literature including a methodological quality assessment. Arch Pediatr Adolesc Med 2012;166(1):49-55.