The Internet has been growing at an incredible rate since the past decade, importantly due to the ubiquity of smart devices that enhance the access to the Internet. The prevalence of Internet usage rocketed from 6.5% in 2000 to 43% in 2015 throughout the world. However, the widespread of the Internet brings up some new problems, such as Internet Addiction (IA). The percentage of IA was 24.8% to 27.8%, and it was in a rapid uptrend. According to Parasuraman et al. (2017), IA had negative impacts on physical, psychological, and social health among students, such as depression, insomnia, and attention deficit hyperactivity disorder (ADHD). So, are children under 18 years of age who have Internet addiction at increased risk for cardiovascular diseases compared with children under 18 years of age without Internet addiction?
In “Effects of Internet Addiction on Heart Rate Variability in School-aged Children,” a group of nursing professors from Taipei Medical University conducted a cross-sectional study to examine the relationship between IA and autonomic regulation. One of the interesting facts was that heart rate variability (HRV), as a method to diagnose cardiovascular diseases (CVD), showed frequency of fluctuations of heart rate and blood pressure and indicated function of autonomic nervous system. In addition, Internet addicts had lower high frequency (HF) of 36.1% and higher low frequency (LF) of 64.0%. On the contrary, non-addicted had higher HF of 46.9% and lower LF of 53.1%. As LF indicated both parasympathetic nervous system (PNS) and sympathetic nervous system (SNS) function and HF indicated PNS function, Internet addicts had higher SNS activity and lower PNS activity. Lastly, such decreasing HRV put Internet addicts in a high risk of CVD.
“Reliability is concerned with the consistency of a measurement method”. In the Lin et al. (2014) article, the researchers used The Chinese Internet Addiction Scale (CIAS) to “assess symptoms of Internet Addiction”. The CIAS had strong reliability because “Cronbach’s alpha coefficient was .93, and the test-retest reliability was 0.76”. The Pittsburgh Sleep Quality Index (PSQI) used to “assess subjective sleep quality” had “Cronbach’s alpha coefficient of .76 and a 2-week test-retest reliability of .70”. Therefore, PSQI had acceptable reliability. The type of reliability was test-retest reliability. Participants filled out both questionnaires with detailed explanations from the researchers. Additionally, the researchers gathered electrocardiographic data through “a monitoring system” and all variables were under controlled. Therefore, the Lin et al. (2014) article had strong reliability in both measurement methods and data collection.
“The validity of an instrument is a determination of how well the instrument reflects the abstract concept being examined”. The type of validity in the Lin et al. (2014) was evidence of validity from contrasting groups because the researchers divided the subjects into Internet addicting groups and Internet non-addicting groups. The results of both the CIAS and PSQI questionnaires yielded contrasting scores as expected. The CIAS had “a sensitivity of 67.8%, a specificity of 92.6%, and a diagnostic accuracy of 87.6%” (Lin et al., 2014, p.494). The PSQI had “a sensitivity of 98% and a specificity of 55%” (Lin et al., 2014, p.494). So, the Lin et al. (2014) article showed validity in the measurement tools.
In critiquing the Lin et al. (2014) article, both strengths and weaknesses presented. One weakness of the study was that the researchers used the convenience sampling method, which created bias, as evidenced by all subjects chosen from “a local elementary and junior high school” (Lin et al., 2014, p.494). The closer Cronbach’s alpha gets to 1.0, the more reliable the article is (Grove et al., 2015). Therefore, one strength of the study was having strong reliability with Cronbach’s alpha of CIAS being .93 and Cronbach’s alpha of PSQI being .76 (Lin et al., 2014).
In the “Public Health Implications of Excessive Use of the Internet, Computers, Smartphones and Similar Electronic Devices Meeting report” published by World Health Organization (2015) website showed typical clinical presentations of IA, including excessive use, withdrawal symptoms, tolerance, neglecting other activities, obsessions, and escapism. It is important for the nurse to identify health problems secondary to IA. One of the most common physical health problems is obesity which is related to sedentary lifestyle, unhealthy diet, and sleep deprivation (“World Health Organization”, 2015). Nurses should provide teaching to the clients that obesity can significantly increase risk of CVD. Nurses, as parts of health care teams, can educate the clients about group therapy, cognitive behavioral, and pharmacotherapy to manage IA (“World Health Organization”, 2015).
Having potential addictive nature, the Internet has gained more attention in the medical field recently. Turel, Romashkin, & Morrison (2016) stated that about 2% to 30% of video game players showed severe addiction symptoms. In addition, videogame addiction could cause cardio-metabolic impairments in adolescents (Turel, Romashkin, & Morrison, 2016). This study also proved that regulating screen times and encouraging adequate sleep could alleviate videogame addiction, improving cardio-metabolic disturbances (Turel, Romashkin, & Morrison, 2016). Even though IA predisposes children under 18 years of age to CVD, nurses can help them by limiting Internet usage and promote good sleep hygiene.
In conclusion, are children under 18 years of age who have Internet addiction at increased risk for cardiovascular diseases compared with children under 18 years of age without Internet addiction? Compared to children without Internet addiction, Children having Internet addiction has reduced HRV, indicating poor autonomic and cardiovascular functions (Lin et al., 2014). A recommendation for nurses is to educate clients about proper control of screen time and adequate sleep in improving cardio-metabolic conditions (Turel, Romashkin, & Morrison, 2016).
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