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LITHUANIAN UNIVERSITY OF HEALTH SCIENCES
ACADEMY OF MEDICINE
PSYCHIATRY DEPARTMENT
Cristina Sánchez-Robles Tre
The influence of new social media on a LSMU students’
group on the psychiatric pathology.
Final Master’s Thesis
Supervisor: Assoc. Prof. Benjaminas Burba
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1 Summary ... 3
2 Santrauka ... 4
3 Conflicts of interests ... 5
4 Permission issued by the Ethics Committee ... 6
5 Abbreviations list ... 7
6 Terms ... 7
7 Introduction ... 8
8 Aim and Objectives of the thesis ... 10
8.1 Aim: ... 10
8.2 Objectives: ... 10
9 Literature review ... 11
9.1 Computer addiction ... 11
7.2 Depression, anxiety, lifestyle and well-being satisfaction ... 14
7.3 Risk predisposing factors ... 16
7.4 The impact of computer addiction on life ... 17
10 Research methodology and methods ... 20
10.1 Study sample ... 20
10.2 Variables determined using the questionnaires ... 20
10.3 Statistical analysis ... 21
11 Results.. ... 22
11.1 Socio-demographic characteristics of the study cohort ... 22
11.2 Determining the relation among possible ICTs addiction and the factor of the age. ... 24
11.3 Lifestyle characteristics of the study cohort ... 26
11.3.1 Sleep pattern ... 26
11.3.2 Anxiety level ... 27
11.3.3 Daily activity level ... 29
11.4 Determining the relation among possible ICTs addiction and different lifestyle factors (anxiety, sleep problems and daily activity). ... 30
11.5 Other acquired addictions on the study cohort, and the relation with being a possible ICTs addict. ... 32
11.6 Comparation of real possible ICTs addicts and their own opinion about themselves as possible addicts ... 34
11.7 Predisposition of certain personality groups to develop ICTs addiction. ... 35
12 Discussion of the results ... 40
13 Conclusions ... 43
13 Practical recommendations. ... 45
14 Literature references ... 46
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1 Summary
An addiction is a psycoemotional and physical disease setting up dependence or need towards a substance, an activity or a relationship. The information and communication technologies (ICT), are a technology complex developed to manage information and send it from one place to another. Among the most daily used ICT are found internet and mobile phones. Therefore, it’s defined as ICT addiction its compulsive, repetitive and prolonged use disabling the control or interrupt their purpose with consequences upon health, social life, familiar, scholar or laboral.
Nowadays, who does not have a mobile phone? Who does not have internet?
We are building and living in a world where almost every person has its own phone with internet. To point out, more and more frequently young people, even kids, have it and cannot stop communicating, gossiping, playing, checking/sharing pictures or videos with each other.
There by, at this point, till where is that healthy for person’s mental state? When does it become malignant for the behavior and mental state? When is it an addiction? Are we all becoming social networks addicts? How much does it affect people during the daily going?
These technologies, obviously do have some positive impacts on the society. Although everything has its positive points, there’s the other coin’s side, which can contain some negative impacts, to mark out in this case: isolation, aggressively behavior or selfishness, anxiety, sleep problems, less active people leading to more pathology.
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2 Santrauka
Priklausomybė yra psichoemocinė ir fizinė liga, sukurianti priklausomybę ar poreikį į medžiagą, veiklą ar santykius. Informacijos ir ryšių technologijos (IRT) - tai technologinis kompleksas, sukurtas informacijos valdymui ir siuntimui iš vienos vietos į kitą. Tarp dažniausiai naudojamų IRT yra internetas ir mobilieji telefonai. Todėl ji apibrėžiama kaip IRT priklausomybė, jos priverstinis, pasikartojantis ir ilgai trunkantis naudojimas neleidžia valdyti ar nutraukti jų paskirtį pasekmėmis sveikatai, socialiniam gyvenimui, pažįstamiems, mokslininkams ar darbiniams.
Kas šiandien neturi mobiliojo telefono? Kas neturi interneto?
Mes statome ir gyvename pasaulyje, kuriame beveik kiekvienas žmogus turi savo telefoną su internetu. Norint atkreipti dėmesį į tai, kad vis dažniau jauni žmonės, net vaikai, turi jį ir negali sustabdyti bendravimo, kūmutė, žaisti, tikrinti / dalintis nuotraukomis ar vaizdo įrašais.
Šiuo metu iki tol, kol jis yra sveikas žmogaus psichinei būsenai? Kada ji tampa piktybine elgesiui ir psichinei būsenai? Kada tai yra priklausomybė? Ar visi esame socialiniai tinklai? Kiek tai paveikia žmones kasdienio važiavimo metu?
Šios technologijos akivaizdžiai daro teigiamą poveikį visuomenei. Nors viskas turi teigiamų dalykų, kitoje monetos pusėje, kuri gali turėti tam tikrą neigiamą poveikį, pažymėti šiuo atveju: izoliaciją, agresyvų elgesį ar savanaudiškumą, nerimą, miego problemas, mažiau aktyvius žmones, kurie sukelia daugiau patologijos.
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3 Conflicts of interests
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5 Abbreviations list
ICTS – Information and Communication Technologies SNS – Social Networking Services
APA – American Psychological Association IAD – Internet Addiction Disorder
IA – Internet Addiction
CIU – Compulsive Internet Use PIU – Problematic Internet Use
DSM – Diagnostic and Statistical Manual of Mental disorders Yrs – years
Y. o. – Years old Ppl – people
6 Terms
Addiction: the fact or condition of being addicted to a particular substance or activity. Addicted: physically and mentally dependent on a particular substance or activity. ICT: technologies that provide access to information through telecommunications.
SNS: online vehicle for creating relationships with other people who share interest, background or real relationship.
Multitask: to deal with more than one task at the same time.
Psychological dependence: state that involves emotional-motivational withdrawal symptoms, upon cessation of drug use or certain behaviors.
Abuse: use (something) to bad effect or for a dab purpose; misuse.
Interpersonal conflict: Conflicts between the addict person and the surrounding people. Social or occupational conflict: Conflicts between the addict person and responsibilities.
Intrapsychic conflict: Decreased effectivity and concentration for a task, and/or with the individual itself.
Prevalence: condition of being prevalent; commonness.
Narcissism: selfishness, involving sense of entitlement, a lack of empathy, and a need for admiration, as characterizing a personality type.
Extroverted: outgoing and socially confident.
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7 Introduction
In the world there are 7.000 million persons, from which more than 5.000 million have on their possession a self-phone. [1] They have become viral. Were supposed to make life simpler let’s take the example of buying something online. Although sometimes when people are not very familiarised with them or the procedure takes time one can just get stacked and mad about the long procedure and mistakes arising from the “not well done followed virtual steps”. So, isn’t better just take a walk to the shop/theatre/museum, buy and get a coffee nearby? Isn’t it healthier?
How often is that a person in a class, in a waiting room, during a visit to the doctor has to check the last notification got in the phone? How many of us is working, watching TV or any other daily activity with the phone right in front or in the pocket? Have you ever looked around in the street while waiting for the bus that almost each person around is checking the phone? Self-phone has become a social instrument in our daily living.
98% of the population in Europe has a computer from which 92% with internet. Among the population below 15 years old 90.2% has a phone. [2] To introduce this topic, three different questions are going to be thrown:
First one, do you know what Information and Communication Technologies are? How many of us do have a phone right here? Does it have internet?
Nowadays, it's very common to have a telephone in our pocket, there prepared to take it any moment it makes a sound. Checking it almost constantly to see new updates, new inputs, new stimulations. Nearly everyone has a computer, a mobile phone, a tablet, something that connects via internet networks. It's crazy how in the last years technologies have quickly developed and we have kindly adapted to them. It's easily available for everybody as well, even kids now have all these technologies.
Information and Communication Technologies (ICTs) are all devices allowing stock, process and transmit information, making such thing immediate, global and portable. [3]
The second question: who is an addict? What is an addiction?
An addiction is defined by the WHO as a physical and emotional disease which has some kind of influence by genetic factors. [4]
9 This topic it is a wide subject that in a way it is overwhelming for our society. It is very interesting how the way of relation with other human being has change as long as the technologies and their availability have been developing as well. People probably has not stopped to think about it, because of what I mentioned before, it has become something that day-to-day usable and helpful (sometimes) that nobody stops to think what are these new devices doing to ourselves and how they impact on our health at a physical, social, mental and working level.
It's relatively a new issue, on developmental way. There are many study’s authors trying to establish different patterns and guidance to give it the importance it has already taken for every person although not many of them have a valuable result, there is need of more investigations and researches as it has expanded over the last 15 years approximately.
I think it is a very interesting topic so that, again who does not have a phone nowadays? Who doesn’t have internet? It's weird for the society's eyes if you do not. Day a day our community is constantly unlocking their self-phone every 10 minutes, forgetting there was people before who survived without all these new facilities. What it is worse, society forgot there is people around us, forgot time is running around us while we are stacked watching our screens.
We are building and living in a world where almost every person has its own internet and virtual connection point accessible 24/7. To point out, more and more frequently young people, even kids, have it and cannot stop communicating, gossiping, playing, checking/sharing pictures or videos with other people. In my opinion, failing to remember they are at this moment here in a real world, with copious authentic activities might satisfy their/our lives.
There by, at this point, can be asked until where is that healthy for human’s mental state? When does it become malignant for the behaviour and mind? When is it a pathology? Is it an addiction? Or is it just a habit, although, when a habit becomes a bad habit? Are we all becoming social networks addicts? How much does it affect people during the daily going? What are the repercussions for individuals?
These technologies, obviously do have some positive impacts on our worlds. Although everything that has its positive points, we have the other coin’s side. Here there are the negative effects. To mark out in this case could be isolation, depression, aggressively behaviour or selfishness, withdrawal, lack of empathy, keep distances between the individual and who used to be his/her friend and family members leading to a pathological habit, in other words, being an addict.
10 television, how could that be possible the easiest way to know about the world, get entertained just from home, there was not need to go out or inquire. It was not an addiction, just needed an adaptation time so the population could get use to the innovating device, adding from their part new attitudes and behaviours towards it and living life from now on. The only difference regarding this theme, is that the TV was created to watch it all together with family or friends.
It was/is used as a collective device. Often, especially in the beginning, during 30's-70's (even nowadays) is used as an action to perform a passive activity with other people, as a group meeting with family or friends. So, it does not really encourage isolation as some ICTs do.
Raised this issue, in this project I would like to check in a group of international LSMU students the amount of time and habits related to the new technologies and internet usage by questionnaires with the aim to find a glance of an addiction on my closest society, how it affects them and which kind of personalities are more prone to fall out on them. Will be observed also if subjects who already have and addiction like alcohol, gaming, tobacco could be, are more susceptible to set an addiction.
8 Aim and Objectives of the thesis
8.1 Aim:
To determine among 100 students if they were possible ICT addicts and the relation with other pathologies. Moreover, check if there are personalities more likely to develop possible addiction. 8.2 Objectives:
1. How many of the student among the sample are possible ICT addicts by ages and gender. 2. To analyse if age is a factor to develop a possible ICT addiction.
3. To evaluate if there is a relation between ICT possible addiction and other addictions
4. To evaluate the significance among possible addicted students with their daily activity, sleep problems and anxiety level of the individual
5. To see the comparation between the possible addicts and if they think they are such.
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9 Literature review
9.1 Computer addiction
On September 2013, 73% of adults in the world were using SNS (social networking sites). In 2016 there were 1.7 billion of Facebook active users. [2] This is just to make an overall idea how ICTs have influenced our lives during the last 25 years, and to keep in mind this a still growing mass.
Several studies showed that apple users unlock their phone around 80 times per day. [1] Thereby it can be explained that some places as cinema or theaters are closing their doors or real material books are not being sold.
According to WHO an addiction is a repeated use of a psychoactive substance, to the extent, user (addict) is periodically or chronically intoxicated, shows a compulsion to take the substance, has great difficulty in voluntary ceasing or modifying its use, and exhibits determination to obtain substance by almost any means.[4] It is a psycoemotional and physical disease setting up dependence or need towards a substance, an activity or a relationship. The information and communication technologies (ICT), are a technology complex developed to manage information and send it from one place to another. [5] Among the most daily used ICT are found internet and mobile phones. [3] Therefore, it’s defined as ICT addiction its compulsive, repetitive and prolonged use disabling the control or interrupt their purpose with consequences upon health, social life, familiar, scholar or laboral.
When to appeal to this phenomenon, technological addiction, as a “disease”? Somehow modifies brain’s structure and function. Such as mood, perception, cognition and motor functions. There’s a difficulty to quit the issue of matter. It’s proved that each addict person affects in different ways at least three more persons from their surroundings. Let it be relatives, friends, teachers… It does not involve only patient. Besides, involves closest people’s life. [6]
The development process more usual, trying a new conduct. Individual likes it and starts doing it more frequently. On and on, his usage becomes much usual. At the end, becomes a bad habit when instead of a satisfaction conduct is used to change a bad feeling, to relief discomfort. Then pleasure is not that satisfying, and the usage of the conduct becomes shorter period of time and less intense. Subjects show less capacity to cope with daily frustrating situation trying to avoid them, or being the only way to escape from stressful situations by the use of these conducts developing what it’s already mentioned, an addiction. A scheme: trying; usage; next abuse; dependency; last addiction.
12 gaming, digital marketing, social networks (Instagram, Facebook), issues 10 years ago started to show up in the society as a consequence of ICT introduction.
Information and communication technologies (ICT), have many positive impacts are in the learning and development area, [3] for example, online classes in case someone cannot attend “live” due to an important reason; develop physical, mental and social capacities, information it’s at
everyone’s hand. In the health area can be talked about the 3D print machines, follow-up of patients, histories storage; and of course, facilitate communication between people from all the world. As well, they carry negative impacts as was mentioned before some of them. Need of internet, money, frauds, and especially on health: social isolation, loss of intimacy, bad conduct, decrease on teacher’s
credibility, negative comments, anxiety, depression, isolation, aggressively… Five negative aspects from ICT are stress caused by them, misuse, overload of information, multitask and addiction. [7] They can be compared to an automobile, in a way that it has also negative impacts for the owner’s health. It was a new creation at its moment to make life easier and comfortable, although it made people lazier, with physical and psychological negative impacts. The improvement of the machinery and self-computers overcrowd has contributed to an increase of internet users. It is overwhelming how new generations are regularly using them. This growing phenomenon, cannot be yet confirmed as an abuse with a negative effect, there are not yet enough studies.
Therefore, its’s meant to be ICT possible addiction its compulsive, repetitive and prolonged use, disabling the control or interrupt their purpose with consequences upon health, social life, familiar, scholar or work. [1] There are two principal purposes, one is the process of information transmission, and the other the communicative process. The term internet addiction nowadays in google is more popular than in biomedicine sources like medline or psycinfo. [9]
13 According to Echeburua, any satisfactory conduct may become an addictive act depending upon intensity, frequency or money invested on that and the affection of this activity on subject’s relationships. [13] So, ICT are done to make individuals’ life easier, but what happens when there is an obsession with them, is no longer an instrument to help to set a goal. A representative fact from ICT addiction, is the kind of conduct stablished by the subject towards it. It’s a similar addiction as sex addiction, gaming, shopping or working addictions [13]. Among the principal addictive acts are physiological effect, psychological dependence, habitual action, cultural origins, social response, economic-affective maintenance and self-representation. Likewise, worsening conjugal, friendships, work, economy and legal persons’ status [14]. It is how it affects the day a day life of an individual and the interference it can be caused by the usage. Loss of interest on a large number of activities,
recreations and topics. Loss of control and dependency joined to excessive employment [15]. In 1996 was the first time it was considered something like an internet addiction as a psychiatric disorder. It was classified as lack of impulses control by the American Psychological Association (APA). Other related terms used in the last 25 years, internet addiction (IA) (Young 1998b), Internet addiction disorder (IAD) [16], computer addiction (CA), compulsive internet use (CIU) [14], problematic internet use (PIU). On 2006 Hollander, claimed that there was a new category for this disorder in the Research planning agenda for the DSM-V in which it was included, c) part, called: disorders included on at the present time at non- specified disorders: Internet addiction or to computers, impulsive-compulsive sexual conduct and compulsive shopping. [17] [18] [19]
Other authors, aim it might be the way to project an underlying addiction to something else. It acts as an intermediator, as it could be to bet in a Casino. The use of technologies is not a problem, nor a pathology. What can develop a pathology is the constant use. Turns out to a bad habit and finally pathology. To conclude, a bad habit can result in an addiction. [20]. In contrast, Holmes, claims if the employment of internet is with the goal of occupational or study issues it might not be consider an addiction. Must be recognize an interference on its life.
Some of the traits to diagnose it are the dependence it creates (Echeburua 1999, Griffits 2000), lack of control, growing a tension feeling inside or even craving for the use. It must last according to DSM-IV 12 months. [11][13][21]. The behavior is done even when tried not to perform it. The use of new technologies must not settle apart our feelings and emotions. This is psychological dependence. [9]. There is the need to draw a line between its usage, addiction and dependence. To measure dependence and whether it’s a habit or a bad habit. Quality of life and life satisfaction can be assessed. [22]
14 matter its purpose [23]. Population use to spend around 10-15 hours average per week. Overall, it’s expected that those persons more likely to develop internet addiction would spend more than that amount of time on internet [24][25].
Mark Griffiths (1995) already started writing about the increasing phenomenon
“technological addiction” as a behavioral dependence covering no human interactions which are far from the classic dependence definition, although it is left a match point with psychological
dependencies. He thought there are six core components that compound a behavior named addiction which are, salience, mood modification, tolerance, withdrawal symptoms, conflict and relapse. Defined that if a subject matches all six traits there is an addiction. [11]
It turns out when there is not enough control upon the utilization. Depends on individual characteristics and their ability to self-regulation and harming [26]. Harm includes all the negative aspects mentioned before (depression, social and interpersonal conflicts…)
Another author, Goldberg proposed the first internet disorder definition taking as a criterion the DSM-IV. He defines it as internet addiction disorder as a behavioral addiction. Symptoms are similar to those persons who suffer a substance abuse. According to him, it changes subject’s life when there are present three out of these symptoms in a 12 month period of time: 1) tolerance, 2) abstinence, 3) impossibility to restrict time for its purpose, 4) wises, failing to avoid action, 5) plenty of time invested for internet actions, 6) leave other activities as social, leisure or occupational activities, 7) persistent use leading to phycological, physical, occupational or social problems. He used the term internet addiction as a mal-adaptive use of the internet. [22][27]
Nestor Fernandez Sanchez (2013), based on chemical addictions, says, there is not an internet addiction as it appeals before, there are bad conducts on the road to all the utilities provided by
internet. Can be called a behavioral addiction [28].
7.2 Depression, anxiety, lifestyle and well-being satisfaction
It’s seen a relation between the use of social networking and several psychiatric disorders, including depression, anxiety or low self-esteem. When a person feels depressed, feels the need of isolation, probably those feelings increase the use of this networks, which affects negatively.
15 individual think, “why other people are happier than me?” Or “how is possible other people’s lifestyle is more dynamic (better) than my life is?”, “life is not fair”. Incrementing negative sensations in the brain, expressed as negative effects [29].
Another study from cyberpsychology, behavior and social networking displayed the fact the more time people spent going out with their friends, the less they agreed with the aim that other’s life is better and they are happier. When you meet somebody out, you may become Facebook friends, which is more balance than just adding random friends because the person is already known and subject knows about circumstances surrounding that person. [30]
There is a hypothesis thrown by the International Journal of an Emerging Transdiscipling in Informing Science about the relation of technological addictions and the impact on our lives and well-being which shows the relation of the factors developing a SNS addiction (diminished impulse control, distraction, social influence and satisfaction), the negative impact on our day by day life and negative outcomes on quality of life, satisfaction and well-being. [23]
It seems that Facebook, to name a SNS and internet are used to make stronger and tighter relations among people who doesn’t meet or see each other that frequently. The result has negative effects on already patients with depression symptoms and positive effects on other subjects. Those beneficial effects are due to the support feeling contributed for the individual in short term use while extensive use, may lead to depression and isolation. [31][34][41][42]
A study about prediction of declining in subjective well-being in young adults by Facebook use was made by the psychological department of Michigan’s university. [32] The purpose was to investigate whether the use of SNS are only to fulfill basic human’s social need for communication rather than enhance well-being. Results showed the more people used Facebook, the worse
subsequently they felt, satisfaction levels dropped off over time.
Good apportions from ITC, for lonely people, or people who is far away from home, SNS could be a nice tool in order not to feel isolated or alone. It contributes to a psychological satisfaction, synchronicity, and provides sort of security for the owner of a phone, in this case. It supplies the chance for immediately and availability somebody needs to establish direct contact.
16 There are certain personalities more likely to develop addictions, particularly internet
addictions. Some of those features seen in subjects under researches, people with maturation deficits, impulsivity, frustration intolerance, lack of self-control, difficulties to postpone wishes or poor communication skills or lack of self-esteem. Other researches mention there might be even biological factors. Physiological predisposition. [33]. Extroverted personalities show less predisposition for a gambling addiction, nevertheless, more for SNS addiction, while introverted people showed the opposite. [34]
Self-esteem were determinant patterns in both the general use and problematic use of ICTs. High neuroticism and low self-esteem were associated more often with problematic usage, related to the need of looking for new experiences and stimuli against boredom.
Another study for psychological actions to find more likely personalities to develop an addiction. Sample of people from 18 to 21 years old was grouped on how long they use their self-phones. 1st : less than 0,5 hours/day; 2nd : 2 or more hours/day, 3rd: more than 4 hours/day. After, some
traits were evaluated to analyze which of them were more expressed. Conclusion seen are that those subjects more sensitive, easily vulnerable, impulsive and disorder indifferent showed a high use of self-phones because they think it affects positively to their well-being. On the other hand, extroverted and animated group of people used the phone more than 4 hours, as they have more contact with people, have a bigger need to relational frequently, share what they do, interact and approval. While secure and self-efficient subjects may see it as a waste of time so they do not develop addiction. Therefore, subjects could find internet as a way to realize stress or avoid that anxiety in poor relation skills participants. [35]
Chozil’s study in 2012 showed 62.7% of participants failing in reduce phone’s utilization from which 27.8% rather prefer phone contact and avoided face to face contact. It shows ICTs are used among young people as a tool to cope with situations. [36]
7.3 Risk predisposing factors
Younger people in study age, university people, are more likely to undergo addicted.
Expressly adolescence is a period of lifetime when a person looks for its own identity and position in the world. Furthermore, this group might be more vulnerable to the addiction for the reason that could develop a pseudo identity. At this age is when individuals have the higher level of relation among different surrounding people therefore if through internet, they found it comfortable they can be
17 for them is highly available the use of computers or phones as an excuse studies, getting distracted by the devices may help them, with the consequence of bad university or school results. [39]. It has been appreciated young population has a large number of network friends while those in the “silver age” (over 60’s) a minimal amount of SNS friends. [41].
A sedentary lifestyle or bad habits are demonstrated to be risk factors for developing this disorder. Night living persons who stay till late hours at night waked up. Night users need more prevention than diurnal.
Lonely people to feel the gap might use these utilities for instance to get distracted and spend the spare free time on something that may provoke good feelings or satisfaction, videogaming,
chatting, discovering… Leo Sang Min Whang in 2003 with some Korean researchers made an investigation concerned about the increase in their country of internet in relation with the high speed internet almost every home owns, using not minutes, hours in front of a screen worried about the isolation. It showed some addicted and many had highly risk for addiction. Personalities of those population matched with the criteria of anxiety or lonely individuals with connecting with other people socially. [31][34][39][40][41].
Echeburua thinks main risk factors for this psychological weakness are: [13]
1. Fragile personality, low self-stem, somebody looking for new either emotions. 2. Lack of interpersonal relationships owing to being shy or social phobia. 3. Cognitive deficit, spared attention, uncontrolled fantasies or easily distracted. 4. Psychopathological alterations: chemical addictions or psychopathologies (depression, etc.)
Another study made, questioned a positive correlation between internet addiction and phobic anxiety and paranoid ideation, results were positive, presenting high risky behavior to develop it with a tendency for addiction [37].
7.4 The impact of computer addiction on life
Prolonged implication on these media has consequences at different levels. First level: physical changes:
18 most affected activity decreased due to ICTs use, which causes one of the main human physiological dysfunctions expressed in a human [24]. Another study to demonstrate the association between
Facebook dependence and poor sleep quality (showed a prevalence of poor sleep quality on the 55.5% of the sample. Having a strong influence those Facebook dependent users on daytime dysfunction than non-dependent. [38].
Tiredness, the fact that the subjects stays awake at night makes him/her not sleep enough hours (6-8 hours recommended for a normal adult) to feel reload after one day, carrying this fatigue the whole day.
Immune system and cancer. Weakness, as a consequence of the previous mentioned conditions, sleep and tiredness, patient could be more susceptible for opportunistic or seasonal infections. Cancer risk for infants and children. Children below 2 years old should be far away from tablets and electronic devices.
Sedentary lifestyle and nutritional irregularities. Giving up on other activities owing to lack of time. To skip or forget meals.
Traumatic pathologies. Accidents crossing the street due to lack of attention on the surrounding. Carpal tunnel syndrome: too long time because of the hand’s position located on the mouse’s computer. Cervical and spine problems, to look constantly to the screen on standing position, places the neck on an unnatural way, inducing pressure to cervical intervertebral discs, deforming that spine level. Easier to grow a hump.
Migraines and ocular problems. Abuse of technological devices, develop hypermetropia or myopia due to prolonged visual contact with the screens
Second level: Personal level, including the following consequences:
Behavioral changes, restless, irritable, impatient after prolonged contact or/and we should take special interest to when cannot access to ICT. High impact on concentration, learning abilities and academic performance, especially on young population. Isolation: wasting that many hours in front of the screen causes person be alone for extended time periods.
Histrionic and narcissism conducts fomentation. Need of approval on extroverted
19 depression and loneliness. Self-concept, what one believes about them and identity is an important component of it. Humans need to maintain or raise their self-esteem unconsciously. Effects of narcissism and self-esteem have been tried in some studies and hypothesis testing. Displaying lower esteem personalities spent more amount of time on the media (especially SNS) while high self-esteem individuals used it, not dependently with different purposes. [34][39][40][41][42].
Introverted people, on the other hand, in this context may show a benefit by reason of compensating their lack of social skills and communications by internet chatting.
Despite personalities as some author claims, everybody is similar in many characteristics, as a person’s concept as a totality, there is not found an accurate definition, it’s said to be as how a subject think, feels (internal bearings) and acts (external bearing) accordingly to certain situations. [38][40].
Familiar and friendship consequences: increasing the time in front of the screen may leave social relation with family left apart, deteriorating relation between parents and son or between friends.
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10 Research methodology and methods
It is a descriptive study made on a sample of LSMU students using as tools a questionnaire and some statistics operations with the aim of analyzing the impact of the ICTs use on their behaviour, mental and physical health as a consequence.
10.1 Study sample
In this research I took a sample of 100 LSMU international students males and females whose ranged age was 18-31 years of age. All volunteered subjects were informed and consented before their participation on the study.
There were not specific criteria for the subjects far away from being LSMU international student. Their task was to answer a given questionnaire. Meanwhile answering the questionnaires there was a person to clarify any doubt they had.
10.2 Variables determined using the questionnaires
The questionnaire had 31 questions plus age and academic year, made by myself. Trying to stablish different patterns. They were divided according to three main groups: first, lifestyle of the individual; second, several questions about their ICTs habits; third, personality characters. Some of them were not used at the end for the study, as they were not meaningful. There was one answer to pick except in the last question that could be chosen more than one character.
a) Group 1: 11 questions. I obtained information about their daily activity, anxiety level, sleep problems and other addictions such as smoke, drinking, etc. Sample had to pick one answer.
b) Group 2: 19 questions. Were evaluated answers about internet devices use frequency, how it could affect their emotions, socially, if is used as a satisfaction goal, if it interferes with concentration or attention, if was tried to limited or stopped their use or lied about it. Some authors affirm if all those are positive there is an addiction, I took as a reference authors from the literature review. According to them, was set if they were possible addicts to ICTs or not. Being the criteria equal or more than 3 positive answers from this group of questions. Some of them were not relevant for the study thus were not used.
21 10.3 Statistical analysis
Descriptive statistics were calculated for variables included in the data analysis. All data were written down into the computer and analyzed using excel for some simple relations and aggrupation while more complex analyses employing Statistical Package for Social Sciences (SPSS).
On excel all data was collected from the questionnaires using “=COUNT.IF.GROUP” . Answers where change to numeric system, 1 as response yes, 0 as no and 2 as maybe. Afterwards 2 (maybe) were pondered in the yes/no answers in order to get more reliable numbers. This was needed to rise the criteria if subjects were possible addicts or not, if they had other addictions meant before or to evaluate some aspects of their lifestyle as active/not active. Also, all those simplified data were submitted on SSPS in order to obtain results.
SSPS program was worked with Student’s T test method in cases with two variables (possible addicts, activity, sleep problems and other addictions). Student’s T test compares two averages, tells if they are different from each other and how significant are. Significance level assumed p<0.05.
22
11 Results.
In the results, all the data are based or compared with the possible addicted participants, being them the main variable.
11.1 Socio-demographic characteristics of the study cohort
In this chapter are presented the characteristics of the questionnaires’ participants. (Figures 11.1.1)
Figure 11.1.1 Distribution of total (1st block) cohort and split on females and males (2nd-3rd block) divided by group of ages. (Blue colour-18-21 y. o.; Orange colour – 22-24 y. o.; Green colour - 25-27 y. o.; Yellow colour – 28-31 y. o.)
As we can see in figure 11.1.1 the distribution of group ages among people from 18-24 years old is bigger than people among 25-31 years in this study, while the difference between females and males is nearly equal, being 51 females and 49 males. There are 32 individuals among 18-21 y. o. from which 23 are females and 9 males. We found the dominant group of the study here, there are 34 participants in the group 22-24 y. o., being 12 females and 22 males. Between 25 and 27 years old were found 22 total participants, 12 females and 10 males. Last, the minority group, ranged 28-31 y. o. there are only 4 females and 8 males.
23 Figure 11.1.2 Graph of total sample of participants divided by non-possible addicts, and addicts by ages.
Figure 11.1.3 Distribution of total possible ICTs addicts on the first column’s block and division of
possible addicts divided by age and gender, second and third blocks. (Blue colour-18-21 y. o.; Orange colour – 22-24 y. o.; Green colour - 25-27 y. o.; Yellow colour – 28-31 y. o.)
Table 11.1.1 Table with percent of participants picking positive answers from the fields to determine
criteria for possible ICTs addiction.
FIELD FREQUENCY EMOTIONAL SOCIAL LIED STOP SATISFA. GOAL CONCENTR. ATTENTION P. % 98% 46% 61% 16% 56% 47% 78% 30% 27% 13% 18% 12%
Total sample
TOTAL P. AD 22-24 TOTAL P. AD 18-21 Non-addicts (possible) TOTAL P. AD 25-27 TOTAL P. AD 28-31 27 30 18 12 0 21 11 11 4 0 6 19 7 8 0 5 10 15 20 25 30 35 Individuals by agesNumber of possible addict participants
24 Distribution of the participants, total and among ages and gender is as the follow. It was found that 86 from the total sample, 100 participants, were possible ICTs addicts (Figure 11.1.2). From those 86 subjects, they were classified according to age groups as the follow (Figure 11.1.3). In the first group, 18-21 y. o. were found 27 possible addicts. Of those who were in that collective, 21 were females and 6 males. It’s appreciated a wide difference between genders having those years. In the second group (22-24 y. o.) there were 30 participants as possible addicts, from which 11 females and 18 males. Among this group which is the most prevalent as possible addicts, the gap between genders is formed by 7 participants. Here, it’s more prevalent among the male group. In the third group (25-27 y. o.), containing a total of 18 participants, 11 females and 7 males, the rate of total possible addicts has decreased and difference among genders is not wide. In the fourth group (28-31 y. o.), the total number of possible addicts is 12, being the less prevalent. From those, 4 were females and 8 males. Although difference between genders is not too wide, males’ double females in this case.
It is good to take a look on the percent subjects obtained in general about the criteria for matching or not the possible addiction (Table 11.1.1). Criteria were 3 or more positive fields made from positive questions related to those. Fields were frequency, emotional, social, lied about it, satisfaction goal, affection on concentration or/and attention and have tried to stop it. Results from subjects were, 98% of them, had positive frequency field, was the field with the highest prevalence. The field less prevalent was lying about the use of ICTs, with 16% of the participants. On the way between them were emotional field with 46% of the participants, 61% with positive social field, 56% of participants tried to stop using ICTs but only 16% succeeded on not using them and last, 47% of participants use these new social media and devices as a satisfaction goal.
11.2 Determining the relation among possible ICTs addiction and the factor of the age.
Table 11.2.1 Relation between different group ages and ICTs possible addicts by LMS
and Scheffe.
Dependent variable: ICTs addicted ppl
25 22-24 18-21 ,039 ,083 ,975 -,20 ,28 25-27 ,064 ,093 ,923 -,20 ,33 28-31 -,118 ,114 ,785 -,44 ,21 25-27 18-21 -,026 ,094 ,995 -,29 ,24 22-24 -,064 ,093 ,923 -,33 ,20 28-31 -,182 ,122 ,528 -,53 ,16 28-31 18-21 ,156 ,115 ,605 -,17 ,48 22-24 ,118 ,114 ,785 -,21 ,44 25-27 ,182 ,122 ,528 -,16 ,53 L LMS 18-21 22-24 -,039 ,083 ,645 -,20 ,13 25-27 ,026 ,094 ,786 -,16 ,21 28-31 -,156 ,115 ,176 -,38 ,07 22-24 18-21 ,039 ,083 ,645 -,13 ,20 25-27 ,064 ,093 ,490 -,12 ,25 28-31 -,118 ,114 ,304 -,34 ,11 25-27 18-21 -,026 ,094 ,786 -,21 ,16 22-24 -,064 ,093 ,490 -,25 ,12 28-31 -,182 ,122 ,138 -,42 ,06 28-31 18-21 ,156 ,115 ,176 -,07 ,38 22-24 ,118 ,114 ,304 -,11 ,34 25-27 ,182 ,122 ,138 -,06 ,42
Table 11.2.2 Relation between different age groups and possible ICTs addicts by Duncan and Scheffe. ICTs addicted ppl
Age N alfa = 0.05 // 1
Duncana,b 25-27 22 ,82
26 22-24 34 ,88 28-31 12 1,00 Sig. ,115 Scheffea,b 25-27 22 ,82 18-21 32 ,84 22-24 34 ,88 28-31 12 1,00 Sig. ,390
After testing possible addicted participants and their division by ages as was explained in the previous chapter, can be seen there is not a relation between their age and the possible addiction. Table 11.2.1 and table 11.2.1 prove that fact as significance (<0.05) in the three Anova test was bigger than that in every age group, therefore, it is not significant. Among this sample there is not a relation among age and possible addiction.
11.3 Lifestyle characteristics of the study cohort 11.3.1 Sleep pattern
In this chapter of the master thesis, are evaluated by ages, the sleeping problems in the last period of time, before questionnaires were given, participants could have experienced.
Figure 11.3.1.1 Participant(s) expressed by percent with sleeping problems against participants
expressed by percent without sleeping problems. Blue colour represents participants between 18-21 years old; orange colour 22-24 y. o.; grey colour 25-27 y. o.; yellow, 28-31 y. o.
Can be observed in figure 11.3.1.1 that there is not such a big difference talking about participants with and without sleeping problems, thus 59 of them claimed to have had against 41 claimed not have had.
From those 59 individuals with sleeping problems, 37%, the most prevalent group were 22 34%
29% 20%
17%
Without sleeping problems
18-21 22-24 25-27 28-31 31% 37% 24% 8%
With sleeping problems
27 people between 22-24 years old; 31% represents 18 participants among 18-21 years old; 24%, 14 participants among 25-27 years; smallest group with quite less difference of participants with an 8%, 5 people between 28-31 years.
From the other 41 individuals without sleeping problems, the biggest group (14 participants) aged 18-21 years which is the 34%; following 29% with 12 participants aged 22-24 y. o.; next, 20% with 8 participants aged 25-27 y. o.; narrowest group with the 17% from these participants between 27-31 y. o., 7 participants.
Seeing that the last group with 28-31 years old have less sleeping problems comparing their number of participants on both without sleeping problems and also are the less with sleeping problems. Having a significant gap of percent to the closest group.
11.3.2 Anxiety level
In this chapter, will be evaluated the anxiety level the participants had in the last 3 months when the questionnaire was given. The options were if they did not experience any anxiety at all in the last three months, or if they did. In case they did, they had to pick a number which represented their anxiety between 1-10 being 1 nearly no anxiety and 10 as maximum level. From their answers, 1-3 was mild level, 4-6 moderate level and 7-10 severe anxiety level. In the next graph will be shown the collected data divided by age groups.
Figure 11.3.2.1 Participants who experience anxiety vs. participants who did not divided by ages. 22 27 16 10 10 7 6 2 18-21 yrs 22-24 yrs 25-27 yrs 28-31 yrs 0 5 10 15 20 25 30
28 Figure 11.3.2.2 Anxiety level (mild, moderate, severe and none) on participants divided by age groups
Figure 11.3.2.1 shows on the blue line the participants who suffered from anxiety vs. orange line showing participants who denied to have suffered from anxiety during the last 3 months. It is represented as the follow on the division by age groups: 22 participants claimed have had any kind of anxiety against 10 participants aged between 18-21 years. 27 subjects had a positive answer against 7 who didn’t among the ages 22-24 years old. Between the ages 25-27 years old, 16 said yes vs. 6 who did not. On the last group, 28-31 years, 10 participants affirmed vs. only 2 who denied.
To point out, it’s curious that 10 is the number of participants on the first group (18-21 y. o.) did not experience anxiety, and among the forth group (28-31 y. o.) is the maximal number of participants that did suffer, what could be interpreted as anxiety increasing decreasing with age, maybe due to more self-control, maturation level and knowing of the self-individual.
Checking next figure 11.3.2.2, it shows, on the first block of columns which represents mild anxiety, 7 participants were 18-21 y. o., 8 p. were 22-24, only 3 p. were 25-27 y. o. and 4 p. were 28-31 y. o. On moderate anxiety block, among 18-21 y. o. 7 participants; among 22-24 y. o. 5 participants; among 25-27 y. o. had 7 participants; among 28-31 y. o. 4 participants again as in mild. Last block, which shows severe anxiety, goes like this: between 18-21 y. o. 8 participants; between 22-24 y. o. 14 participants; between 25-27 y. o. 6 participants; between 28-31 y. o. only 2 participants.
It’s observed a peak among ages 22-24 years on mild and severe anxiety. Highest differential peak on severe with 14 subjects. Severe anxiety level is the leading group with a total of 30 participants, far from mild and moderate levels with 22 and 23 people respectively.
Mild Moderate Severe Deny to have had
0 2 4 6 8 10 12 14 Nº p ar tic ip an ts
29 11.3.3 Daily activity level
In this chapter, data from individual’s lifestyle habits, hobbies and others were evaluated to obtain a positive or negative value about activity on their daily routine (yes for active/no for non-active). Results obtained are shown hereby.
Figure 11.3.3.1 Active vs non-active participants shorted by ages.
Collected data shows on figure 11.3.3.1 16 participants active vs. 16 participants not active, which is equal, not significant activity level between 18-21 years old group. Were found 19 participants active vs. 15 not active between 22-24 years old, seeing more active people among this collective. Between 25-27 years old, 14 participants were active vs. 8 p. non-active, here, also predominated active people against non-active. Last group, between 28-31 years old, 8 subjects active vs. 4 non-active. As this last group is smaller, activity numbers as well are decreased.
Could be observed among the total sample (n=100) there is more active participants than non-active. The most active group seeing on 22-24 years old.
18-21 22-24 25-27 28-31 0 2 4 6 8 10 12 14 16 18 20
30 11.4 Determining the relation among possible ICTs addiction and different lifestyle factors (anxiety, sleep problems and daily activity).
Table 11.4.1 Relation between possible addicts and their sleep problems by T-test.
Levene T test for equality average
F Sig. t gl (bil) Sig. Dif
ave-rage error Dif
95% (interv confianza) dif Inf Sup Sleep pro-blems Equal va-riances assumed 2,617 ,109 -1,2 64 98 ,209 -,174 ,138 -,448 ,099 Not assu-med equal varian -1,1 46 14, 89 1 ,270 -,174 ,152 -,498 ,150
Table 11.4.2 Relation between possible addicts and their activity level by T-test.
Levene T-test for equality averages
F Sig. t gl Sig. (bil ) Dif medias Dif error 95% (inter. Conf.) Dif. Inf Sup Ac-tive life Equal va-riances assumed 1,273 ,262 -,427 98 ,67 0 -,064 ,149 -,359 ,232 Not assu-med equal vari. -,423 15,70 ,67 8 -,064 ,150 -,383 ,256
Table 11.4.3 Relation between possible addicts and their anxiety level by Scheffe and LSM.
Dependent variable: ICTs addicted ppl
31 SEVERE ,009 ,096 1,000 -,26 ,28 MODE-RATE NO MILD -,014 -,083 ,099 ,102 ,999 ,881 -,29 -,37 ,27 ,21 SEVERE -,074 ,095 ,894 -,34 ,20 SEVERE NO ,060 ,092 ,936 -,20 ,32 MILD -,009 ,096 1,000 -,28 ,26 MODERATE ,074 ,095 ,894 -,20 ,34 LMS NO MILD -,069 ,100 ,490 -,27 ,13 MODERATE ,014 ,099 ,888 -,18 ,21 SEVERE -,060 ,092 ,518 -,24 ,12 MILD NO ,069 ,100 ,490 -,13 ,27 MODERATE ,083 ,102 ,417 -,12 ,29 SEVERE ,009 ,096 ,925 -,18 ,20 MODE-RATE NO -,014 ,099 ,888 -,21 ,18 MILD -,083 ,102 ,417 -,29 ,12 SEVERE -,074 ,095 ,436 -,26 ,11 SEVERE NO ,060 ,092 ,518 -,12 ,24 MILD -,009 ,096 ,925 -,20 ,18 MODERATE ,074 ,095 ,436 -,11 ,26
Table 11.4.4 Relation between different anxiety levels among possible addicted participants by Duncan
and Scheffe
Anxiety level Alpha = 0.05 // 1
Duncana,b MODERATE 23 ,83
NO 25 ,84
SEVERE 30 ,90
MILD 22 ,91
Sig. ,444
Scheffea,b MODERATE 23 ,83
NO 25 ,84
SEVERE 30 ,90
MILD 22 ,91
Sig. ,866
32 found between them.
On table 11.4.2 activity of participants was tested with their possible addiction to analyse the relation as it could have been that less active or more active participants showed more or less predisposition to be possible addicts. That table testing by T-test (p<0.05), shows significance values above 0.05, meaning there is not a relation between possible addict participants from the sample and their daily activity level.
Last point from this chapter to test, was if the anxiety level of participants has something to do with their possible addiction to ICTs. Table 11.4.3 and table 11.4.4 show the significance values (<0.05), among different anxiety levels and possible addict participants. As can be seeing, all values from those tables are bigger than 0.05, which means there is not a relation among those two facts.
11.5 Other acquired addictions on the study cohort, and the relation with being a possible ICTs addict.
In this chapter it is determined the participants who already have an addiction, as it was asked on the questionnaire directly by questions nº 7, nº 8, nº 9 and nº 27. They were about alcohol, smoking and other habits that could cause an addiction. Among the smokers, there were different options from which certain of them being daily smokers has been considered as addiction. Another positive answer in this chapter was a positive answer on nº 27 which directly asked “Would you consider you have any other addiction?”.
Table 11.5.1 Table containing smoker and non-smoker participants from the study collected in age
33 Figure 11.5.1 Graph showing the percent of participants non-smokers, those who are possible
smoke addicts and which from them are also possible ICTs addicts or not.
Data collected were, 41 participants with other possible addiction against 59 without any other addiction. Focusing on smokers, was determined that 27 of the total participants (100) were possible smoking addicts.
Smoking data from participants were (figure 11.5.1), on the one hand 65 participants do not smoke at all, on the other hand, 35 participants smoke, frequently or occasionally. From those, 27 are possible smoking addicts as mentioned before.
Checking if the participants who present a possible smoking addiction have as well ICTs possible addiction, turned out that 24 (24%) of those 27 do confirm both possible pathologies, only 3 (3%) only as a possible addiction smoking. That fact could prove that an already acquired addiction can cause another addiction easily.
Taking a look on table 11.5.1, it is observed smokers separated by ages. 10 smoker participants between 18-21 years old, 10 as well between 22-24 years old, 4 in the range of 25-27 years and 3 individuals were 28-31 years. There is a drop from 25 years old on, it means smokers rate decreases with age, like in the ICTs possible addiction.
Table 11.5.2 Significance (<0,5) by T-Student test between an acquired addiction and possible ICTs
addiction.
Levene T- test for equality averages
F Sig. t gl Sig. (bil) Dif ave-rage Dif error 95% (interv confianza) dif Inf Sup Other addic-tions Same va-riance assu-med ,62 6 ,431 ,56 -2 98 ,575 -,082 ,146 -,372 ,208 Smoking +, ICT + 24% Smoking +, ICT -3% Smoking -73%
-34 Not assumed same va-riance -,53 7 15, 345 ,599 -,082 ,153 -,408 ,244
Last issue to solve, was if it could be found a relation among having already an addiction such as smoke, or another and to be a possible ICTs addict. T-Student test (p<0,5) was performed to evaluate the relation and as it was seeing on table 11.5.2, significance is more than 0.05, which means a negative significance, there is not a relation among those two characteristics.
11.6 Comparation of real possible ICTs addicts and their own opinion about themselves as possible addicts
In this chapter is going to be tested the difference between the real possible addicts to ICTs were obtained in the previous chapters and how many of them actually really think they are or they think they are not and according to the study that’s not real. Data about their own opinion were obtain from the question nº 27, “Would you consider yourself addicted to internet/technologies use?”. Results are the following.
Figure 11.6.1 Participants who think are possible ICTs addict’s vs reality (participants who are possible
ICTs addicts) and participants who don’t think they are addicts vs. those who are not.
YES NO 0 10 20 30 40 50 60 70 80 90 YES; 51 NO; 49 YES; 87 NO; 13
35 Figure 11.6.2 Participants self-opinion comparing with reality about being possible ICTs addicts.
In figure 11.6.1 can be appreciated that 51 participants from all the sample, total of 100, confirm they are addicted to internet and technologies while the true according to this study is that almost all of them are, 87 possible ICTs addicts and only 13 are not, as we already mentioned in previous chapters.
Figure 11.6.2 shows that 49% of participants, which is half of the sample, confirm by themselves a possible addiction to new media and technologies, being proved by the study. They are aware about their condition while 38% of participants are not aware of that as they affirmed, they do not consider themselves possible technologies and internet addicts, or they don’t want to recognize it, as if could have a bad connotation for some people although, study said they indeed are possible addicts. Only 2% of individuals (2 participants) picked they think they are possible addicts even though they not meet criteria from this study in order to affirm could be considered possible addicts. The yellow cheese from the graph which represents 11% of the sample, have the opinion they are not possible addicts and they are right because they are part of the 13 participants who are not matching the criteria for possible addiction.
11.7 Predisposition of certain personality groups to develop ICTs addiction.
In this part of the project, personalities of the sample are going to be discussed. In the questionnaires, last question was which of the following features, represents yourself best. The different characteristics were the following: happy, stable, serious, traditional, conservative, confident, insecure, openminded, outgoing, sensitive, introvertive, like new things, don’t like changes. Participants could choose as many as they wanted to.
Those features are grouped on different personality risk levels to develop a possible SNS or ICTs addiction.
49 2
38
11 THEMS. + W/POSSIBLE ADDICTION
THEMS. + W/O POSSIBLE ADDICTION THEMS. - W/P. ADD
36 On group A, which is high risk. Could be found the items: happy, extroverted, insecure, openminded, sensitive and like new things. Mainly, extroverted participants.
On group B, which is intermediate risk, were: stable, confident, openminded, introvertive, happy, outgoing and serious. Mainly, confident participants.
On group C, which is low risk, were mainly the features traditional, conservative and don’t like changes.
Figure 11.7.1 Distribution of personality traits among the participants from the study. Blue colour
represents participants who presented that trait, grey colour participants who does not.
37 Table 11.7.1 Different personality traits among participants. On the first column, nº who presented the
feature, on the second column from participants in the first column the ones with possible ICTs addiction.
TOTAL + W/Possible ICT addiction Happy 73 60 Stable 64 53 Traditional 27 22 Confident 57 46 Conservative 23 21 Insecure 11 9 Open minded 78 68 Serious 35 30 Outgoing 41 30 Sensitive 51 44 Introvertive 25 20
Like new things 64 52
Don’t like changes 22 20
Table 11.7.2 Comparation of personality types analysing their significance (p<0,5) by Scheffe and DMS
among possible addicts.
Dependent variable: ICTs addicted ppl
(I) Personality type (J) Personality type Diferencia de medias (I-J) Desv. Error
Sig. Intervalo de confianza al
38
Type C Type A ,150 ,089 ,095 -,03 ,33
Type B ,063 ,091 ,490 -,12 ,24
Table 11.7.3 Significance between personality type and possible addicts by Duncan and Scheffe. (p<0,5)
Personality type N
Subconjunto para alfa = 0.05
1
Duncana,b Type A 41 ,80
Type B 37 ,89
Type C 22 ,95
Sig. ,101
Scheffea,b Type A 41 ,80
Type B 37 ,89
Type C 22 ,95
Sig. ,221
On figure 11.7.1 can be made an overall of personality features between the sample. In each column the participants from the study who believe they present that trait, on blue colour. Participant who did not mark that trait are presented in each column as grey colour. 73 participants though they are happy; 35 serious; 64 participants thought they are stable; 27 traditional and 23 conservative; 57 picked confident while only 11 insecure; 78 of them chose open minded; 41 participants extroverted while 25 introvertive; 51 participants are sensitive; 64 like new things whereas 22 don’t like changes.
Most prevalent traits are happy and open minded, in young people may be typical. Features which were presented by more than half of the sample are, stable, confident, happy, open minded, sensitive and like new things. Among those characteristics chosen by more than 50% are some which could belong to intermediate group (stable, confident) and some which belong to high risk group (like new things, sensitive). From low risk level, participants who picked their features were less than 30% of the total sample, don’t like changes 22 participants, traditional 27 participants and conservative 20 participants.
39 from the total with a 22% is represented by type C.
40
12 Discussion of the results
Overall in the study, has been found most of the sample were possible ICTs addicts, 87% of them followed the criteria. Meaning, technologic phenomenon and newly developed social networks have a bigger and bigger impact on young people as long as time goes on. Has been presented younger individuals were in fact more likely to show it.
Dividing our cohort by ages and gender, turns out to be, 32 participants among 18-21 y. o., be-ing 23 males and 9 females. 34 participants were 22-24 y. o., 12 females, 22 males. Belongbe-ing to the 25-27 y. o. group a total of 22 individuals, 12 females, 10 males. Ending, in the 28-31 y. o. group, 12 people, being 4 females and 8 males. These data, showed that, the difference among females and males is not really significant. Results were, 51 females against 49 males. The difference obtained was two more individuals from the female group, seeing the gender difference is not relevant to the use of ICTs. Nevertheless, cannot be forgotten to mention the study limitations, participants were not totally random and sample was not big enough, so this information is not really significant. Moreover, at the current time, it’s not a “gender” topic as it could be years ago. That’s why there was not a long re-search on that topic during the study.
As claimed before, the prevalence peak among participants to develop a possible addiction ac-cording their age, was shown to be the group between 22-24 years old, represented as 30%. Followed by 18-21 years old on the second place, represented as 27%. (According to graph’s results). A study made on Malaysian students showed similar age results, more than 70% belong to the younger groups. [48] Other studies also present the difference of ages as an influencing factor to develop an addiction, showing young participants more vulnerable, more than 74% of participants were aged between 18-25 y. o. [22] It could be explained as the younger the people are, the more predisposed to develop a possi-ble addiction. Meanwhile, when people grow up, the less likely to develop an addiction. In our study groups aged 18-24 y. o. are as well dominant. Despite that fact, here, the top group was aged 22-24 y. o., perhaps again, by reason to limitations of the study.
41 After checking lifestyle characteristics from the sample, was found the group which displayed deeper expressed sleep problems, higher index of daily activity and suffer more often severe anxiety level, were those from 22 till 24 years old. Followed, once again, by those from 18-21 years old. Those factors (anxiety, activity and sleep problems) drop afterwards as age increases. Some studies expressed a relation between being stressed or anxious and using more ICTs or SNSs. [46] Alternative studies, present the relation of being depressed, how it affects well-being and the need to maintain SNS con-nection. [26][32] Those conditions stimulate anxiety, drawing a cycle of needs. [30][46] Another study made on Peruvian students, demonstrated almost 70% of Facebook users in their sample, where more than half had poor sleep quality and more daytime dysfunction. [38] In contrast, our study results prove, anxiety, sleep problems and activity index, aren’t related to the possible ICTs addiction, signifi-cance is >0.05 (sig. <0.05).
Some of the sample participants, 27%, affirmed they are smokers, which is an addiction. Study showed there was not a relation between those participants who presented an already known or recog-nized addiction and the ICTs possible addiction, p >0.05 (p<0.05). Even though, certain authors and studies try to demonstrate how it’s easier to develop a new addiction when an individual already pre-disposes one, linked to the impulses control. [6][13]
Was interesting to compare how many of the participants thought they are addicted to technolo-gies and social media and reality about them. We had 49% of the sample realistic, they affirmed their technological devices need and indeed, they present a possible ICTs addiction, plus 11%, who denied an addiction and they were not possible ICTs addicts. Regardless, 38% from the sample, did not think they are addicts, sadly, study expressed them as possible addicts. Only 2% of these sample was wrong with their thoughts, in the way they though they are addicts, while they are not considered so. These results can be used to have a little approach how some individuals do lie to themselves to get fewer negative inputs in their life, not to disturb their well-being.
42 a situation they think might not control face to face. The least predisposed one, type C, most conserva-tive thinking personalities. Last one, is shown in farther studies not to be affected at all by ICTs and SNSs. [34] [35] [39]
43
13 Conclusions
I. In the present study, large majority of the sample were possible addicts, 87 people. Peak among the 22-24 y. o. group, being females the biggest gender group division.
II. Results show, there is not a relation among age and the possible ICTs addiction.
III. After analyzing lifestyle characteristics: anxiety, sleep problems and activity index, none of them is related to the possible ICTs addiction.
IV. Study showed there was not a relation between those participants who presented a recognized smoking addiction and the ICTs possible addiction.
V. Most of the participants, 60, were right about their possible ICTs and SNS addiction. VI. Different personality types do not affect the predisposition to develop a possible ICTs
44 Study limitations.
45
13 Practical recommendations.
As the technological phenomenon grows, population becomes more use to employ it in their everyday living. Actually, there is a new developing phobia between adolescents called nomophobia, which is phobia to not to have the phone with you. It should be advised for new generations and already trained on ICTs generations, some guidelines in order to keep a safe use, avoiding this project’s issue, develop an addiction. As it has been proved many young people are by now possible addicts.
Some tips are:
- To limit technological devices use, especially in young ages - Set a time schedule for joy devices use and accomplish it - Education
- Not to answer messages or emails immediately - To plan free time
- Changes on lifestyle, to enhance other activities as reading, cultural activities, cinema, music, sports, outside activities
- To stimulate communication face to face between family and friends - To work out on team tasks (eg. Volunteering, social activities)
