The internet is an integral part of daily life (Correa, Hinsley, & de Zúñiga, 2010), used in fields such as education, research, information exchange, communication, trade, and entertainment (Goel, Subramanyam, & Kamath, 2013). However, its rapid spread and individuals' loss of control over its use has resulted in internet addiction (Üneri & Tanidir, 2011), which is defined as a breakdown in social, professional, and interpersonal relations caused by progressively increasing internet use (Young, 2007).
Internet addiction can cause serious psychological, social, and physical harm in all age groups, but adolescents are the most vulnerable (Tacyildiz, 2010). A basic characteristic of adolescence is the search for excitement and novelty. The constant updating and development of the internet and the wealth of online activities appeal to this age group. The internet allows people to hide their identity or take on an identity that they choose. This “invisibility” makes the internet more attractive than real life for young people who want to portray an ideal personality (Ceyhan, 2008).
Statistics show that 70.6% of internet users worldwide are adolescents (International Telecommunication Union, 2017). In a study of 13,284 adolescents between ages 14 and 17 in seven European countries, the prevalence of internet addiction was 1.2% and the risk of internet addiction was 12.7% (Tsitsika et al., 2014). In a study performed in six Asian countries with 5,366 adolescents between ages 12 and 18, the prevalence of internet addiction ranged between 6% and 21% (Mak et al., 2014). According to a study conducted in Turkey with individuals ages 6 to 15, the use of computers, the internet, and mobile phones was 60.5%, 50.8%, and 24.3%, respectively. In this study, computer, internet, and mobile phone use were highest among the 11- to 15-year-old age group (Statistics Institute of Turkey, 2013).
Internet addiction has adverse effects on adolescents' physical, psychosocial, and mental health. In one study, adolescents with internet addiction experienced back and neck pain (Gür, Yurt, Bulduk, & Atagöz, 2015). In another study, academic and school performance was lower in adolescents who were addicted to the internet than adolescents without addiction (Yu & Shek, 2013). Internet addiction causes a breakdown in interpersonal relationships, isolation from society, and loneliness (Savci & Aysan, 2017). In a study in Korea conducted with adolescents, a significant correlation was found between obsessive-compulsive and depressive symptoms and internet addiction (Jang, Hwang, & Choi, 2008). In a study conducted with high school students, it was found that those who used the internet for ≥5 hours per day had significantly high levels of melancholy, suicidal thoughts, and suicide attempts (Messias, Castro, Saini, Usman, & Peeples, 2011).
Internet addiction has been studied by various professional groups, including psychiatrists, psychologists, sociologists, and nurses (Chou et al., 2017; Eroglu & Bayraktar, 2017; Karacic & Oreskovic, 2017). Nurses can cooperate with professionals in other disciplines to work with adolescents with internet addiction problems (Ayar et al., 2017). The World Health Organization and International Council of Nurses have specified nurses' roles in the fight against addiction (Rassool, 2010). Accordingly, nurses must take on the roles of teacher, therapist, counselor, developer of health, and researcher when working with those who have a problem with addiction, including internet addiction (Oh, 2005). Nurses can identify young people at risk of internet addiction, perform routine checks, assess for risk factors, work with families, and conduct education and counseling (Ayar et al., 2017; Kilic, Avci, & Uzuncakmak, 2016). Adolescents with internet addiction may have ineffective coping skills, anxiety, risk of loneliness, risk of suicide, social isolation, and insomnia. To address these problems, nurses can apply interventions from the Nursing Interventions Classification, such as strengthening coping, cognitive reconstruction, counseling, and anxiety reduction (Bulechek, Butcher, Dochterman, & Wagner, 2012). In addition, nurses can follow up and provide professional support for adolescents with internet addiction problems through public mental health centers and youth counseling centers (Yoo, Cho, & Cha, 2014).
The aim of nursing research is to form a foundation for evidence-based practices and nursing interventions (Bayik, 2004). A systematic review is a research method in which the strongest evidence is produced to guide evidence-based practice (Karaçam, 2013). Systematic reviews form strong infrastructures for future therapeutic interventions by combining the results from many studies (Shamseer et al., 2015). Study results are reflected in practice, provide a basis for science-based nursing interventions, and improve the quality of nursing care (Gorak, 2003).
The aim of the current systematic review was to examine nursing studies on adolescents with internet addiction to provide the most up to date information and determine priorities for future research.
The current study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (Moher, Liberati, Tetzlaff, & Altman, 2009).
A literature search of six databases (Science Direct, PubMed, EBSCO Host, Ulakbim Medical Database, Turkish Psychiatry Index, and Türk Medline) was conducted between August and September 2018 to include studies performed between 2009 and 2018. The literature review was conducted in Turkish and English. Keywords used were “internet addiction,” “nursing,” “adolescents,” “problematic internet use,” “internet addiction, nursing, adolescents,” and “problematic internet use, nursing, adolescents.”
Inclusion and Exclusion Criteria
Inclusion criteria were studies on internet addiction with an adolescent sample, included nurse researchers, published in Turkish or English between 2009 and 2018, and for which full text was accessible.
Studies not included were those not related to internet addiction, conducted in groups other than adolescents, did not include nurse researchers, and published outside the inclusion range (i.e., 2009 to 2018) or in languages other than Turkish and English. Studies for which only an abstract was available and review studies were also excluded.
Selection of Studies
A total of 881 articles were accessed as a result of the literature search. These articles were transferred to EndNote X8 and duplicate articles (n = 109) were eliminated. Articles were examined by title and abstract, and an additional 396 articles were eliminated. The remaining 376 articles were examined according to inclusion and exclusion criteria, and another 341 articles were eliminated. Thirty-five studies were included in the systematic review (Figure 1).
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (Moher et al., 2009) flowchart.
Data Extraction and Analysis
Studies were examined in regard to the country in which the research was conducted, research design, concepts or variables examined in relation to internet addiction, and results. Concepts or variables were classified in four categories: mental health, physical health, psychosocial health, and other. This classification was first performed independently by each researcher and then together to provide a common view. The proportion of these concepts or variables was calculated as a percentage. Findings were given in accordance with this classification. In addition, data regarding sociodemographic characteristics and characteristics related to internet use were also collected.
Quality Assessment of Studies
Study quality was assessed using evaluation instruments for observational cohort and cross-sectional studies, case-control studies, and experimental studies prepared by the National Heart, Lung, and Blood Institute (NHLBI; n.d.). The evaluation criteria of these three instruments, used to evaluate internal validity and the risk of bias (selection bias, information bias, measurement bias, or confounding), are similar to one another. Researchers independently rated the quality of the studies as good, fair, or poor (NHLBI, n.d.).
Thirty-five studies conducted in Turkey, Korea, Taiwan, China, Jordan, and Poland were examined. Of these studies, 80% (n = 28) had a cross-sectional design, 14.3% (n = 5) had a prospective cohort design, and 5.7% (n = 2) had an experimental study design. Table 1 shows the concepts or variables related to internet addiction that were examined. Of these variables, 43.4% focused on mental health, 43.4% on psychosocial health, 8.8% on physical health, and 4.4% on other areas.
Concepts and Variables Examined in Included Studies Regarding Internet Addiction in Adolescents
The sociodemographic factor with the greatest correlation to internet addiction in adolescents was male gender. Variables relating to internet use with the greatest correlation to internet addiction were online gaming and amount of time spent on the internet/computer (Table A, available in the online version of this article).
Sociodemographic Variables Correlated with Internet Addiction and Characteristics Relating to Internet Use
Mental health variables found to be most related to internet addiction were depression, anxiety, alcohol or substance use or addiction, and suicidal thoughts or attempts. Psychosocial characteristics relating to family dynamics, relationship with parents, and low success at school were correlated with internet addiction. Regarding physical health, insomnia and obesity were found to be correlated with internet addiction (Table B, available in the online version of this article).
Table C (available in the online version of this article) shows the results of the two experimental studies, in which programs related to healthy internet use and self-regulatory efficacy were found to be effective in reducing internet addiction.
Experimental Studies and Features
Quality Assessment of Studies
Of the studies included, 10 were rated good, 21 as fair, and four as poor. In the studies rated poor, factors constituting risk bias were lack of clarity in the calculation of the sample size, lack of a clear explanation concerning blinding, and lack of checking for the effect of potential confounding variables.
In the current systematic review, which was conducted with the aim of examining nursing studies on the effect of internet addiction on adolescents, most studies had a descriptive design. Similarly, in another systematic review that examined 64 studies on internet addiction, 85.9% had a descriptive design (Al Sheibani, 2015), indicating descriptive methods were preferred to experimental methods in most studies. Internet addiction is a relatively new concept, having only been studied for the past 20 years, thus it derives from an effort to determine the factors affecting internet addiction and those that are correlated with internet addiction. However, studies have shown that internet addiction has many negative effects on adolescents (Ha & Hwang, 2014; Lin et al., 2014).
Although qualitative research methods were not among the exclusion criteria of the current study, the authors found no examples of qualitative research on internet addiction conducted in the field of nursing. When studies performed on this topic were examined, the small number of qualitative studies compared with quantitative studies was remarkable. In a meta-synthesis examining 10 qualitative studies on internet addiction, Douglas et al. (2008) proposed a theoretical model on internet addiction. A standard has not yet been set with regard to the measurement of internet addiction and there is no definite conceptual framework, which makes it difficult to develop nursing practices. Therefore, qualitative studies in the nursing field need to be performed to better understand the concepts related to internet addiction, strengthen the conceptual framework, and create a nursing care model for internet addiction.
One interesting result of the current study was the small number of prospective cohort studies on internet addiction performed by nurses. Results obtained in prospective studies are stronger than those found in other non-experimental methods. This method gives researchers a better chance to examine the incidence and possible reasons for the problem (LoBiondo-Wood & Haber, 2010). In a systematic review in which only prospective cohort studies were included, it was recommended that all stages of development from childhood to adulthood be comprehensively investigated in future prospective studies. In addition, it has been stated that studies should be conducted over a longer period and with closer monitoring (Anderson, Steen, & Stavropoulos, 2017).
In the current research, characteristics such as depression, school success, and relationship with family and parents were the most examined variables relating to internet addiction in all studies examined. Least examined concepts were loneliness, personality traits, psychosocial behavioral problems, neck pain, burning eyes, and cyberbullying. When systematic reviews on this topic were assessed, the variables examined most to determine a correlation to internet addiction in adolescents were depression, alcohol and substance use, relationship with family and parents, and satisfaction with life. The least examined variables were schizophrenia, loneliness, boredom, obesity, eyesight, immune system effects, sleepiness, and cyberbullying (Al Sheibani, 2015; Anderson et al., 2017; Kuss, Griffiths, Karila, & Billieux, 2014). In accordance with these findings, it can be said that studies focused mostly on mental and psychosocial health and did not adequately address physical health.
In the studies examined, the sociodemographic variable that was most frequently found to be correlated with internet addiction in adolescents was male gender. Similar results are found in the relevant literature (Hsieh et al., 2016; Yu & Shek, 2013). Systematic reviews have shown that males are at greater risk of internet addiction (Anderson et al., 2017; Kuss et al., 2014; Müller, Levien, & Albernaz, 2018) compared to females. It has been suggested that the link between male gender and internet addiction may arise from type of internet activity. For example, males more often choose internet activities such as online gaming and cybersex (Chou, Condron, & Belland, 2005). Male gender is also a known risk factor in disorders related to alcohol and substance use (Kessler et al., 2012). In a study by Kessler et al. (2012), it was found that the risk of developing disorders related to substance use was 30% to 80% greater in male adolescents than female adolescents, suggesting that males may have generally weaker defenses against addiction.
Variables related to internet use that were found to be most correlated with internet addiction in adolescents were online gaming (Chang, Chiu, Lee, Chen, & Miao, 2014; Yayan, Arikan, Saban, Gürarslan Bas, & Özel Özcan, 2017) and length of time of internet or computer use (Wu et al., 2016; Yayan et al., 2017). In prospective studies, online gaming was a strong predictor of internet addiction (Anderson et al., 2017). Online gaming is recognized as a potential addiction, and certain personality traits and individuals' capacity for self-regulation may increase the risk of online gaming addiction (Kuss & Griffiths, 2012). In a systematic review by Müller et al. (2018), a positive correlation was determined between the length of time of internet use and internet addiction, and it was found that adolescents with an internet addiction spent twice as much time on the internet as adolescents without such an addiction. An increase in time spent on the internet can be interpreted as an indicator of tolerance, as with alcohol and substance use (Kuss et al., 2014).
Internet addiction has a negative effect on mental health. In particular, correlations were found between internet addiction and depression, anxiety, alcohol and substance use or addiction, and suicidal thoughts or attempted suicide (Chang et al., 2015; Jang & Ji, 2012). In a study by Cho, Sung, Shin, Lim, and Shin (2013), individuals who showed signs of anxiety and depression in childhood had an increased risk of internet addiction in adolescence. In a systematic review by Müller et al. (2018), a correlation was found between internet addiction and anxiety, thoughts of suicide, and social phobia. In many studies, a correlation was found between internet addiction and mental health, but it was not clear whether these pathologies were a cause or result of internet addiction. On the other hand, it has been suggested that the internet could be a method to be used in coping with mental illnesses such as depression and anxiety (Kuss et al., 2014). Therefore, it is important to take into consideration the correlation between internet addiction and psychopathologies while providing care for adolescents with internet addiction problems.
In relation to psychosocial health, family-related characteristics, such as family conflict and functionality; parent-related characteristics, such as physical and psychological neglect; and low school success were found to be related to internet addiction (Chang et al., 2014; Hsieh et al., 2016; Jang, Kim, & Choi, 2012; Yen, Ko, Yen, Chang, & Cheng, 2009). Similarly, in a systematic review by Anderson et al. (2017), it was reported that in adolescents, parental and family-related factors were correlated with the level of internet addiction, and a home environment with good communication reduced the risk of internet addiction. It was also stated that there was a positive correlation between low family functionality and internet addiction, and it was suggested that adolescents used the internet as an ineffective coping method in managing stressors that originated in the family (Anderson et al., 2017). Lack of a reliable and supportive family environment increases the risk of internet addiction (Kuss et al., 2014). In accordance with these findings, it is important that families participate when care is given to adolescents with internet addiction problems. In addition, there is agreement in the literature that there is a two-way relationship between internet addiction and school success. Success at school is a protective factor against internet addiction; however, lack of success at school is a risk factor for internet addiction (Anderson et al., 2017).
Internet addiction has effects not only on mental and psychosocial health but also physical health. In the current systematic review, a correlation was found between internet addiction and insomnia and obesity (Li, Deng, Ren, Guo, & He, 2014; Lin, Kuo, Lee, Sheen, & Chen, 2014). Bener et al. (2011) also showed a positive correlation between excessive internet use and obesity and poor eyesight. In another study, insomnia, snoring, apnea, and nightmares were more common in adolescents with internet addiction than those without internet addiction (Choi et al., 2009). Excessive internet use results in adolescents sitting at the computer for long periods, which can lead to lack of sufficient physical exercise and obesity (Li et al., 2014). Individuals who use the internet excessively have irregular eating habits. Rather than having meals, they prefer to snack on food that can be eaten quickly in front of the computer. This way of eating can easily be a cause of obesity (Ruoran, Weihua, & Xiong, 2011). In addition, excessive internet use can disrupt adolescents' sleeping patterns and lead to insomnia (Lin, Kuo, et al., 2014).
In the experimental studies examined in the course of the current research, it was determined that programs implemented to prevent internet addiction in adolescence were effective (Uysal & Balci, 2018; Yang & Kim, 2018). In another systematic review, all five studies examined reported positive results in the prevention of internet addiction (Bagatarhan & Siyez, 2017). However, there are also studies in which the program implemented made no difference in the levels of internet use (Busch, De Leeuw, & Schrijvers, 2013). This finding may be due to differences between programs. In addition, there were few studies on preventing internet addiction (Vondrácková & Gabrhelík, 2016). Thus, there is a need for more studies of this type.
Experimental studies on internet addiction are in the form of prevention programs implemented in vulnerable groups. Studies show that the number of individuals attending internet addiction outpatient clinics is steadily rising (Beutel, Hoch, Wölfling, & Müller, 2011; Senormanci, Konkan, Güçlü, & Senormanci, 2014; Thorens et al., 2014). Studies performed with at-risk groups are important, but there is also a need for studies on nursing care practices for internet addiction.
The current review has a number of limitations. First, in line with the aims of the review, only studies with an adolescent age group were included; studies with university students were not included. In addition, only studies performed by nurse researchers were included in the review. The reason for this was to determine the current state of nursing research on the topic of internet addiction and to provide guidance for the development of nursing interventions. These limitations should be kept in mind when interpreting the findings and conclusions. In addition, as internet addiction was the main concept examined, studies that focused on online gaming addiction were excluded from the research. Future systematic reviews could perform comparative research of specific types of internet addiction, such as online gaming.
Second, the included studies were conducted in Turkey, Korea, Taiwan, China, Jordan, and Poland. Thus, the research results can only be generalized to those countries. In addition, there was no discussion of whether culture might have an effect on the development of internet addiction. Future studies could focus on cultural factors that may affect internet addiction. Other limitations are that only six databases were searched for relevant studies, gray literature was not evaluated, and studies in languages other than Turkish and English were not included. Thus, conclusions and recommendations are limited to the studies included in the research.
Implications for Nursing Practice
In light of the findings of the current review, variable and invariable risk factors relating to internet addiction were determined. Nurses can refer to the recommendations in the Nursing Interventions Classification (Bulechek et al., 2017) when providing care to adolescents with internet addiction. Variable risk factors and nursing interventions are provided in Table D (available in the online version of this article).
Modifiable Risk Factors and Nursing Interventions
Studies examined in the current review were mostly cross-sectional, with prospective cohort and experimental designs used less often, and qualitative research methods not used at all. Thus, the relationship between internet addiction and psychopathologies is not clear. Therefore, it is recommended that (a) prospective cohort research methods be used to clarify this relationship and provide stronger evidence of factors relating to internet addiction; (b) experimental research methods be used to develop programs to prevent internet addiction in adolescents; and (c) qualitative research methods be used to better understand the concepts relating to internet addiction to strengthen the conceptual framework and form a nursing care model to address internet addiction.
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Concepts and Variables Examined in Included Studies Regarding Internet Addiction in Adolescents
|Mental Health (%)||43.4|
| Depression||7 (8)|
| Alcohol and substance use/addiction||4 (4.5)|
| Self-esteem||4 (4.5)|
| Suicidal ideation and attempt, self-harm behavior||3 (3.3)|
| Anxiety||3 (3.3)|
| Coping (proneness to boredom, perceived social support)||2 (2.2)|
| Psychological symptom screening||2 (2.2)|
| Aggression||2 (2.2)|
| Self-perception (self-rated health, subjective happiness)||2 (2.2)|
| Behavioral and emotional problems||2 (2.2)|
| Social phobia||1 (1.1)|
| Smartphone addiction||1 (1.1)|
| Loneliness||1 (1.1)|
| Personality traits||1 (1.1)|
| Affective temperament profiles||1 (1.1)|
| Hopelessness||1 (1.1)|
| Sexual violence||1 (1.1)|
| Posttraumatic stress disorder||1 (1.1)|
|Physical Health (%)||8.8|
| Sleep-related features (sleep quality, sleeping problems)||2 (2.2)|
| Obesity||2 (2.2)|
| Heart rate variability||1 (1.1)|
| Physical behavior problems (nutrition, sleep, activity)||1 (1.1)|
| Burning eyes||1 (1.1)|
| Neck muscle pain||1 (1.1)|
|Psychosocial Health (%)||43.4|
| Family and related features (conflict, function, economic status, support, intactness, monitoring)||13 (14.4)|
| Parental and related features a||11 (12.4)|
| School achievement||9 (10)|
| Connectedness to school/peers/parent(s)||3 (3.3)|
| Social skills, communication skills||2 (2.2)|
| Psychosocial behavioral problems (family/friend relationships)||1 (1.1)|
| Positive youth development (cognitive behavioral competence, prosocial attributes, positive identity)||2 (2.2)|
| Physical/psychological/psychosocial status screening||1 (1.1)|
| Cyberbullying||1 (1.1)|
Sociodemographic Variables Correlated with Internet Addiction and Characteristics Relating to Internet Use
|Male gender||Chang et al., 2014; Chang et al., 2015; Gür et al., 2015; Ha & Hwang, 2014; Hsieh et al., 2016; Jang & Ji, 2012; Kilic et al., 2016; Li et al., 2014; Lin et al., 2014; Shek & Yu, 2016; Shek, Zhu, & Ma, 2018; Wu et al., 2016; Yayan et al., 2017; Yoo et al., 2014; Yu & Shek, 2013|
|Female gender||Aylaz, Günes, Günaydin, Kocaer, & Pehlivan, 2015; Liu et al., 2017|
|Age||Çam & Nur, 2015; Malak, Khalifeh, & Shuhaiber, 2017; Wu, Chang, & Tzang, 2014; Wu et al., 2016, Yayan et al., 2017|
|School grade||Choi, Park, & Cha, 2017; Gür et al., 2015; Li et al., 2014; Malak et al., 2017; Wu et al., 2016|
|Vocational high school for men||Kilic et al., 2016|
|Preparing for university entrance examination||Kilic et al., 2016|
|Living in the city||Li et al., 2014|
|Moderate family income||Gür et al., 2015|
|High family income||Malak et al., 2017|
|Low family income||Wu et al., 2016|
|Parental educational level||Kilic et al., 2016|
|Parents divorced||Wu et al., 2016|
|Not living with biological parents||Liu et al., 2017|
|Internet Use Characteristics|
|Internet/computer use time||Choi et al., 2017; Gür et al., 2015; Malak et al., 2017; Wu et al., 2016; Yayan et al., 2017; Yoo et al., 2014|
|Online gaming||Chang et al., 2014; Chang et al., 2015; Gür et al., 2015; Wu et al., 2014; Yayan et al., 2017|
|Chat rooms/chatting||Chang et al., 2015; Malak et al., 2017|
|Social networking/dating sites||Chang et al., 2014; Chang et al., 2015; Yayan et al., 2017|
|Web surfing||Simsek, Akça, & Simsek, 2015; Yayan et al., 2017|
|Pornographic websites/exposure to pornography content||Chang et al., 2014; Chang et al., 2015|
|Exposure to violence on the internet||Chang et al., 2015|
|Presence of computer at home||Aylaz et al., 2015; Li et al., 2014; Ozturk, Ekinci, Ozturk, & Canan, 2013|
|Number of days that can be spent without using the internet||Gür et al., 2015|
|Feeling a need to increase the time spent online||Gür et al., 2015|
|Presence of family members spending a long time on the internet||Gür et al., 2015|
|Thinking that daily internet use is not enough||Gür et al., 2015|
|Age of acquaintance with the internet||Civelek et al., 2016|
|Connecting to the internet from home||Aylaz et al., 2015|
|Going to an internet café||Yayan et al., 2017|
|Connecting with friends via the internet||Wu et al., 2016|
Concepts and variables examined for relation to internet addiction
|MENTAL HEALTH||Depression||1.There is a positive correlation between internet addiction and depression.||(Jang & Ji, 2012; Jang et al., 2012; Malak et al., 2017)|
|2. Depression is an important indicator of continuing internet addiction.||(Chang, et al., 2014)|
|3. Depression is an important variable distinguishing those withand without internet addiction.||(Yen et al., 2009)|
|4. Adolescents with internet addiction have higher depression scores than those without.||(Chang et al., 2015)|
|5.Depressive symptoms in both boys and girls are significantly associated with internet addiction. Among addicted internet users, the frequency of depressive signs was higher infemales than in males.||(Ha & Hwang, 2014)|
|Alcohol and substance use / addiction||1.Alcohol and substance use rates were higher in adolescents with internet addiction than in those without.||(Chang et al., 2015)|
|2. Alcohol and substance use is an important indicator of continuing internet addiction.||(Chang, et al., 2014)|
|3. In both urban and rural areas, the symptoms of internet addiction are more severe in adolescents using alcohol than those who do not.||(Zygo, Potembska, Zygo, Stanislawek, Karas & Pawloska, 2017)|
|4. Levels of problematic internet use were higher in adolescentswho smoked than in those who did not.||(Aylaz et al., 2015)|
|Suicidal ideation and attempt, self-harm behavior||1.Suicidal thoughts and the risk of attempted suicide are higherin adolescents with internet addiction than in those without.||(Lin et al., 2014)|
|2. Internet addiction is significantly correlated with an increased risk of self-harm.||(Liu et al., 2017)|
|Self-esteem||1. There is a negative correlation between internet addiction and self-esteem.||(Jang & Ji, 2012; Jang et al., 2012)|
|2. Self-esteem is lower in adolescents with internet addiction than in those without.||(Chang, et al., 2014; Chang et al., 2015)|
|Anxiety||1.There is a positive correlation between internet addiction andanxiety.||(Jang & Ji, 2012; Jang, 2012; Malak et al., 2017)|
|Coping (proneness to boredom, perceived social support)||1.In adolescents diagnosed with ADHD, lack of external stimulation in proneness to boredom is significantly correlated with a high risk of internet addiction.||(Chou, Chang & Yen, 2018)|
|2.There is a negative correlation between internet addiction andperceived social support and self-control.||(Kılıc et al., 2016)|
|Psychological symptom screening||1. There is a positive correlation between internet addiction and mental health (somatization, obsessive-compulsive disorder, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid thoughts, psychoticism)||(Choi et al., 2017)|
|2.The risk of internet addiction is related to suicidal thoughts, depression, subjective stress, feelings of unhappiness and problematic substance use.||(Yoo et al., 2014)|
|Social phobia||1. There is a positive correlation between internet addiction and social phobia.||(Yayan et al., 2017)|
|Aggression||1.There is a positive correlation between internet addiction andaggression.||(Jang & Ji, 2012; Jang et al., 2012)|
|Self-perception (self-rated health, subjective happiness)||1. Subjective health and subjective happiness values are worse in adolescents with internet addiction than in those without.||(Ha & Hwang, 2014)|
|Behavioral and emotional problems||1. Internet addiction is positively correlated with behavioral problems and hyperactivity and inattention.||(Ozturk et al., 2013)|
|2. Internet addiction is not correlated with affective and behavioral problems (aggression, anxiety-depression, attention problems, blame, social problems, somatic complaints, thought problems, withdrawal, externalization and internalization).||(Wu et al., 2014)|
|Smartphone addiction||1. There is a positive correlation between internet addiction and smartphone addiction.||(Ayar et al., 2017)|
|Loneliness||1. There is a low level positive correlation between internet addiction in male adolescents and loneliness.||(Durualp & Cicekoglu,2017)|
|Personality traits||1. There is a significant difference between internet addiction and extraversion and being open to experience, but there is no statistically significant difference for neuroticism, conscientiousness and agreeableness.||(Ozturk et al., 2015)|
|Affective temperament profiles||1. In adolescents with internet addiction, the frequency of an anxious temperament is higher than in those without. The existence of other temperament types is not correlated withinternet addiction.||(Ozturk et al., 2013)|
|Hopelessness||1. There is a positive correlation between internet addiction and hopelessness.||(Simsek ve et al., 2015)|
|Sexual violence||1.There is a positive correlation between internet addiction andsexual violence.||(Hsieh et al., 2016)|
|Post-traumatic stress disorder||1. Post-traumatic stress disorder, sexual violence, parental physical and psychological neglect (except for maternal physical violence) cause internet addiction.||(Hsieh et al., 2016)|
|PHYSICAL HEALTH||Obesity||1.The rate of obesity is significantly higher in adolescents with internet addiction than in those without.||(Li et al., (2014)|
|2. There is no relation between problematic internet use and obesity.||(Çam & Nur, 2015)|
|Sleep-related features (sleep quality, sleeping problems)||1. The rate of insomnia is higher in adolescents with internet addiction than in those without.||(Lin et al., 2014)|
|2. The rate of problematic internet use is higher in adolescents with sleeping problems than in those without.||(Aylaz et al., 2015)|
|Heart rate variability||1. Internet addiction is correlated with higher sympathetic activity and lower parasympathetic activity.||(Lin et al., 2014)|
|Physical behavior problems (nutrition, sleep, activity)||1. There is a statistically significant difference between students' internet addiction scores and the existence of physical behavioral problems (getting up late, skipping meals, eating at the computer, playing sports on the internet rather than in real life).||(Gür et al., 2015)|
|Burning eyes||1. There is no statistically significant correlation between problematic internet use and neck pain or burning eyes.||(Aylaz et al., 2015)|
|PSYCHOSOCIAL HEALTH||Family and related features (family conflict, function, functioning, economic status, support, intactness, monitoring)||1.There is a positive correlation between internet addiction andthe family's low economicstate.||(Shek & Yu, 2016)|
|2. There is a negative correlation between internet addiction, andfamily functionality and family intactness.||(Jang et al., 2012;Shek et al., 2018)|
|3. Internet addiction is significantly correlated with low maternal occupational socioeconomic status, but not with low paternal occupational socioeconomic status.||(Chou, et al., 2017; Chou, et al., 2018)|
|4. The probability of internet addiction is lower in adolescents with well-functioning families.||(Yu & Shek, 2013)|
|5. Adolescents in families with serious dysfunctionality show a greater tendency to internet addiction than those in families with high functionality.||(Wu et al., 2016)|
|6. Low family monitoring and high family conflict are a distinguishing factor for internet addiction in adolescents, but perceived family support is not a distinguishing factor.||(Yen et al., 2009)|
|7. Internet addiction is not correlated with family functionality, family economic status, family functioning or family intactness.||(Jang & Ji, 2012; Shek & Yu, 2016; Shek et al 2018)|
|Parental and related features (parental mediation, parental marriage status, parental drinking problem, parental behavioral and psychological control, parenting approaches to internet use, mental symptoms in the parent, parental physical violence, parental psychological and physical neglect, parental-child relational quality)||1. Internet addiction is positively correlated with parents' drinking problem, psychological control and physical and psychological neglect.||(Hsieh et al., 2016; Jang & Ji, 2012; Jang et al., 2012; Shek et al., 2018)|
|2. Internet addiction is negatively correlated with parental behavior control and the quality of the parent-child relationship.||(Shek et al 2018)|
|3. Rates of restrictive mediation of parents of adolescents with internet addiction are lower than of those without internet addiction.||(Chang et al., 2015)|
|4. There is a positive correlation between internet addiction and paternal physical violence, but no correlation with maternal physical violence.||(Hsieh et al., 2016)|
|5. In a family with a restrictive parental approach, the probability of adolescents having internet addiction is 1.9 times greater.||(Wu et al., 2016)|
|6.There is no correlation between internet addiction and parental marital status.||(Chou et al., 2017; Choi et al., 2017)|
|7. There is no statistically significant difference between internet addiction and parental mental symptoms (somatization, obsessive-compulsive disorder, interpersonal sensitivity, depression, anxiety, hostility, phobic anxiety, paranoid thoughts, psychoticism).||(Wu et al., 2014)|
|School achievement||1.The academic and school performance of adolescents with internet addiction is lower than that of those without.||(Chang et al.,2014; Yu & Shek, 2013)|
|2. Adolescents with low school success have significantly higher internet addiction scores than those with a high level of school success.||(Gür et al., 2015; Yayan et al., 2017; Yoo et al., 2014)|
|3. Adolescents with medium or poor school success have a higher rate of internet addiction.||(Choi et al., 2017)|
|4.There is a positive correlation between internet addiction andschool success.||(Malak et al., 2017)|
|5.There is a negative correlation between internet addiction andschool success.||(Kılıc et al., 2017)|
|6.There is no significant correlation between internet addictionand school success.||(Aylaz et al., 2015)|
|Connectedness to school / peer / parent||1. In adolescents with low connectedness to parents and school, the probability of starting internet addiction is higher.||(Chang et al., 2014)|
|2. Low connectedness to school in adolescents is a distinguishing factor for internet addiction, but connectedness to the peer group is not.||(Yen et al., 2009)|
|Social skills, communication skills||1.There is a negative correlation between internet addiction and communication skills.||(Kılıc et al., 2016)|
|2. Deficiency in social skills is more severe in adolescents with ADHD who have internet addiction than in those who do not.||(Chou, et al., 2017)|
|Psychosocial behavior problems (family and friend relationships)||1. There is a statistically significant difference between the internet addiction scores of adolescents and psychosocial behavior problems (weakening of family and friendship relationships, activities within the family, feelings of anger and rage when away from the internet, arguments with parents, spending time on the internet rather than with other people, finding life boring without the internet, feeling anger if interrupted when on the internet, feeling discomfort, heart palpitations of trembling when unable to connect to the internet, not doing homework because of online gaming).||(Gür et al., 2015)|
|OTHER||Positive youth development (cognitive behavioral competence, prosocial attributes, positive identity)||1.There is a negative correlation between internet addiction and positive youth development.||(Shek & Yu, 2016)|
|2. Positive youth development indicators do not negatively affect internet addiction behaviors.||(Yu & Shek, 2013)|
|Physical / psychological / psychosocial status screening||1. The physical health, mental health, social health, general health, perceived health and self-esteem scores of adolescents with problematic internet use are lower than those of normal internet users, and their anxiety, depression, anxiety-depression, pain and disability scores are higher.||(Çam & Nur, 2015)|
|Cyberbullying||1. The cyber bullying rates of adolescents with internet addiction is higher than that of those without.||(Chang et al., 2015)|
Experimental Studies and Features
|Study (Year)/Country||Research Design||Program Applied||Duration and Length of Program||Sampling Features||Measuring Tools||Results|
|Uysal & Balci (2018)
Turkey||Experimental, control group pre-/posttest design||Healthy internet use program||3 months, 40- to 80-minute sessions||Middle school students (N= 84)
Experimental group n= 41; control group n= 43||Internet Addiction Scale (Kayri & Günüç, 2009)||Healthy internet use program is effective in reducing internet addiction in adolescents|
|Yang & Kim (2018)
Korea||Quasi-experimental, nonequivalent, control group pre-/posttest design||Self-regulatory efficacy improvement program||10 weeks, one 45-minute session per week||Middle school students (N= 79)
Experimental group n= 38; control group n= 41||Internet Addiction
Proneness Scale (National Information Society Agency, 2003); Self-Control Scale (Tangney, Baumeister, & Boone, 2004); Self-Efficacy Scale (Sherer et al., 1982)||Adolescents in the experimental group had lower internet addiction rates than the control group and higher self-control and self-efficacy levels|
Modifiable Risk Factors and Nursing Interventions
|Modifiable risk factors||Nursing interventions|
|Depression||Mood management, cognitive restructuring, coping enhancement, self-esteem enhancement, emotional support, support system enhancement, behavior management: social skills, humor, hope inspiration, mood/sleep regulation, exercise promotion, family process maintenance, phototherapy: mood/sleep regulation, relaxation therapy|
|Alcohol and substance use / addiction||Substance use treatment, substance use prevention, coping enhancement, support system enhancement, cognitive restructuring, self-esteem enhancement, mood management, behavior management: self-harm, anxiety reduction, support group, anger control assistance|
|Self-esteem||Self-esteem enhancement, mood management, cognitive restructuring, hope inspiration, emotional support|
|Suicidal ideation and attempt, self-harm behavior||Suicide prevention, behavior management: self-harm, security enhancement, crisis intervention, impulse control training, mood management|
|Anxiety||Anxiety reduction, mood management, emotional support, relaxation therapy, calming technique, cognitive restructuring|
|Coping (proneness to boredom, perceived social support)||Coping enhancement, support system enhancement, humor, music therapy, support group, family integrity promotion, family process maintenance, assertiveness training|
|Aggression||Mood management, crisis intervention, anger control assistance, environmental management: violence prevention|
|Self-perception||Self-efficacy enhancement, self-awareness enhancement, self-esteem enhancement, body image enhancement, support system enhancement, developmental enhancement|
|Behavioral and emotional problems||Mood management, behavior management|
|Social phobia||Anxiety reduction, mood management, emotional support, relaxation therapy, calming technique, cognitive restructuring, coping enhancement, support system enhancement|
|Smartphone addiction||Coping enhancement, recreation therapy, distraction, art therapy|
|Loneliness||Coping enhancement, mood management, emotional support, cognitive restructuring, support system enhancement, support group, behavior management: social skills, self-esteem enhancement|
|Hopelessness||Hope inspiration, coping enhancement, mood management, emotional support, cognitive restructuring, self-esteem enhancement|
|Sexual violence||Abuse protection support: child, teaching: sexuality|
|Post-traumatic stress disorder||Trauma therapy: child, coping enhancement, support system enhancement, support group, family integrity promotion, family process maintenance|
|Sleep-related features (sleep quality, sleeping problems)||Sleep enhancement, phototherapy: mood/sleep regulation|
|Obesity||Nutritional counseling, nutrition management, nutritional monitoring, exercise promotion, weight management|
|Physical behavior problems (nutrition, sleep, activity)||Nutritional counseling, nutrition management, nutritional monitoring, exercise promotion, weight management, sleep enhancement|
|Burning eyes||Eye care, dry eye prevention|
|Neck muscle pain||Analgesic administration, massage|
|Family and related features (family conflict, function, functioning, support, intactness, monitoring)||Family integrity promotion, family process maintenance, family therapy, family support, family presence facilitation, family involvement promotion, telephone consultation|
|Parental and related features (parental mediation, parental drinking problem, parenting approaches to internet use, mental symptoms in the parent, parental physical violence, parental psychological and physical neglect, parental-child relational quality)||Parent education: adolescent, parenting promotion, counseling, substance use treatment, substance use prevention, family process maintenance, environmental management: violence prevention, abuse protection support, abuse protection support: child, behavior modification: social skills|
|Social skills, communication skills||Behavior management: social skills, assertiveness training|
|Psychosocial behavior problems (family and friend relationships)||Behavior management, behavior management: social skills, family integrity promotion, family process maintenance, family therapy, family support|
|School achievement||Self-esteem enhancement, emotional support, cognitive restructuring, family process maintenance, behavior management: social skills, assertiveness training|
|Positive youth development (cognitive behavioral competence, prosocial attributes, positive identity)||Developmental enhancement: adolescent|
|Family economic status||Fiscal resource management|
|Internet / Computer usage time||Coping enhancement|
|Internet Activities (Playing online game, Chatting, use of online social networking, Using pornographic websites)||Coping enhancement, recreation therapy, distraction, art therapy|