Screen Addiction Disorder: EEG-based Diagnostics & Trends in Therapeutics

STEVEN RONDEAU, ND, BCN (EEG)

Since childhood, most of us have been told that watching too much television is not healthy and that excessive screen time in any form can have serious repercussions on our general health. In line with popular belief, epidemiological studies have indicated that excessive screen time is an important and independent risk factor for cardiometabolic disorders as well as adult morbidity and mortality.¹ Also, there is evidence that the number of hours of viewing proportionally increases the risk of experiencing socio-emotional problems.² These are some of the main reasons why reducing screen time has been a national health improvement priority and a key strategy of disease prevention in the United States for at least a decade now.³ 

One of the main concerns about screen viewing is its tendency to become addictive. The term “screen addiction” is used to indicate that the engagement with multiple screen activities constitutes a significant proportion of an individual’s daily life and that discontinuation results in to cravings and symptoms, similar to other addictions. Activities that have been found to induce screen addition are those that simply involve a display, including video gaming, messaging and social networking, watching pornography, or binge-watching a television series.^4,5 Importantly, many studies have revealed that a range of psychiatric disorders can coexist with internet addiction,^6-8 including depression,^9,10 anxiety,¹¹ attention-deficit/hyperactivity disorder (ADHD),^12,13 psychosis¹⁴ and obsessive-compulsive disorder (OCD).¹⁵   

Despite the general consensus and supporting evidence that spending long hours in front of a screen is harmful to our short- or long-term health, most people (including many mental health professionals), when asked about the specific nature of damage involved, find it hard to present actual evidence. This is changing as studies use electroencephalography to measure brain waves in people with screen addictions.  

Using EEG to Detect Screen Addiction  

A growing number of research studies are showing that persons with screen addiction exhibit brain activity anomalies, as detected by electroencephalography (EEG). For example, Choi et al found that, compared with healthy controls, individuals with internet addiction showed decreased absolute power on the beta bands at rest, with eyes closed, and that the decrease was proportional to symptom severity and impulsivity.¹⁶ These same individuals also showed higher resting-state absolute power on the gamma band compared to controls.¹⁶ As a reminder, brain waves are categorized according to their relative frequencies, ie, the speed of their oscillations, as measured in the number of waves per second (Hz).¹⁷ Gamma waves have the highest frequency (32-100 Hz), whereas delta waves have the lowest frequency (1-4 Hz).¹⁷ 

Importantly, comorbid depression has also been found to affect the EEG changes associated with internet addiction. For example, in a study by Lee et al, quantitative EEG (qEEG) findings indicated that individuals with internet addiction and comorbid depression exhibited increased absolute delta power relative to patients with internet addiction without depression, and increased relative theta power relative to patients with internet addiction without depression or healthy controls; interestingly, these slower-wave activities have been observed in depression.¹⁸ Similar to Choi’s study mentioned above, the subjects with internet addiction but no depression showed decreased absolute beta activity compared to controls and subjects with both internet addiction and depression.¹⁸ The investigators speculated that lower beta activity might represent “impulsivity and dysfunctional inhibitory control underlying internet addiction.”  

The qEEG study by Choi et al found that, when compared to healthy controls, persons with internet gaming disorder (IGD) exhibited decreased absolute beta power and increased absolute gamma power.¹⁶ In another study, it was shown that greater absolute gamma power significantly correlated with IGD symptom severity.¹⁹ Yet another study found that adolescents with both ADHD and IGD showed greater beta-band power and lower relative delta-band power in temporal regions of the brain compared to an ADHD-only group.²⁰ 

Other data that deserve mention derive from research that employed the recording of event-related potentials (ERP), specifically the P300 (aka P3) wave, during performance of cognitive tasks.²¹ Compared to healthy controls, subjects with excessive internet use exhibited altered P300 activity, specifically significantly lower P300 amplitude and longer P300 latency. Gamma-wave activity (suggested in this article to be around 40-50 Hz) is strongly linked to P300. Partly based on other studies citing similar anomalies, the data suggested that excessive internet use is associated with altered memory and reaction speed, abnormal information coding and integration, and possibly lower brain dopamine.²¹ 

Similarly, a recent ERP study of persons with addiction to cybersex found that, compared to healthy controls, cybersex-addicted subjects had impaired cognitive activity as well as greater impulsivity, as measured by reaction times and by amplitudes of the N2 (200-300 ms) and P3 (300-500 ms) components, respectively.²² 

Altogether, EEG and ERP research suggests that it is possible to identify persons with screen addiction using EEG signals, although comorbidities with other disorders (eg, ADHD and affective disorders such as depression) may complicate the overall profile. Utilizing well-established biomarkers of cognitive and emotion regulation processes, it is possible to determine discrepancies from normative values in view of targeted interventions aimed at normalizing, or at least reducing, the anomalies detected. 

Treatment of Internet Addiction  

Pharmacotherapy & Psychotherapy 

Given the detrimental effects that uncontrolled use of internet-based applications can have on our well-being, the research on targeted interventions has been steadily growing over the last few decades.²³ However, research on the addictive exposure to technology is still in its infancy,^24,25 and studies examining potential treatments have yet to fully inform the design of evidence-based and well-organized treatment plans.  

It has been proposed that the neurobiological changes that take place in the brain of persons with behavioral addictions (eg, gambling) are comparable to those exhibited by people who are addicted to substances.²⁵ Similarly, there could be shared psychological and behavioral characteristics in people with behavioral addictions and those who engage in substance abuse.²⁶ As a result, it is possible that treatments for substance abuse may have comparable effects in persons with non-substance-related addictive behaviors, including those addicted to the use of internet-related technology.²⁵ 

Explored treatments have mainly included psychotropic medications²³ and cognitive behavioral therapy (CBT).²⁷ However, the efficacy of psychopharmacological treatments for technology addiction remains to be demonstrated, especially in large population samples. So far, a number of exploratory case studies support the use of selective serotonin reuptake inhibitors (SSRIs) or naltrexone in internet addiction.²⁸ This is particularly interesting, as naltrexone is typically used to treat alcohol or opioid abuse.²⁹ 

Additionally, bupropion and methylphenidate have been employed for treatment of internet gaming disorder, though with conflicting results. In one study, no differences were found between persons treated with psychotropics versus behavioral therapy.³⁰ In contrast, other researchers found that treatment with bupropion not only led to improvements in mood, but also to significant reductions in the amount of time spent playing video games.³¹ While these contradictory results could certainly be due to methodological limitations, the paucity of studies conducted to date,²⁸ or to the small and heterogeneous patient sample-sizes,²³ it could be argued that the population recruited is still not well known. Without the use of clear, objectively-defined, and consistent criteria to assess the different types of technology used, identifying the most appropriate treatments for those with internet addiction may be challenging.²⁵ In this context, other therapeutic avenues have also been investigated but with similar limitations. For example, some evidence³² suggests that narrative therapy (essentially a process of deconstructing one’s story and then rewriting it) might be effective in the treatment of excessive video game use, and other studies support CBT as a treatment for internet addiction.^27,33 

Targeting Impulsivity  

Clinical studies suggest that impulsive behavior is highly linked to substance abuse,^34-38 and there is evidence that greater impulsivity is associated with poor clinical outcomes^39, 40 and relapse, even after long withdrawal periods.^41,42 While impulsive decision-making and impulsive behavior, in general, appear to be strongly linked to cognitive processes,^43-45 greater physiological arousal and emotional states such as anxiety, anger, sadness, or joy may also be associated with impulsivity.^46,47 

Interestingly, depression and impulsivity share common neurobiological correlates in the brain, including the prefrontal cortex.^48-52 As these anomalies are associated with EEG alterations,^53– 56 EEG-based interventions for addiction have been explored in research studies.  

One such method used to lower impulsivity in persons with substance abuse is EEG-neurofeedback training, a form of biofeedback known to facilitate self-regulation of EEG activity in the brain.^57,58 Importantly, EEG-neurofeedback training has been shown to be effective in the treatment of drug addiction, lowering impulsivity, increasing abstinence rates,⁵⁹ and lowering reactivity to drug-related stimuli.⁶⁰ Similarly, other neurofeedback studies⁶¹ suggest that neurofeedback reduces impulsivity as well as anxiety and depression symptoms in long-term-abstinent addicts, and may also lower the likelihood of relapse.^62-64 

While more research should determine the effects of EEG-neurofeedback training on screen addiction, it is plausible that treatment protocols similar to those used with drug-addiction patients could yield positive clinical outcomes. In any case, the ability to selectively guide interventions targeting specific EEG imbalances is certainly of great advantage given the high level of behavioral heterogeneity exhibited by this clinical population. 

Conclusions 

Excessive screen time can have detrimental effects on general health, including increasing the risk of socio-emotional problems and negatively impacting quality of life. Individuals who are consistently exposed to phones and computers are at increased risk of becoming addicted to screen-watching, while the efficacy of currently available treatments remains to be determined in well-conducted research studies. In particular, while pharmacotherapy and psychotherapy have been shown to have some beneficial effects in this area, other interventions – including nutritional supplementation, natural approaches, and EEG-based interventions (ie, neurofeedback training) – show some promise in reducing functional imbalances in the brain and thus warrant further exploration.   

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Steven Rondeau, ND, BCN (EEG) is a graduate of SCNM. During his studies, he also acquired certification in EEG-Neurofeedback; he later became adjunct faculty in the Mind-Body department. After medical school, he developed the first developmental pediatric naturopathic residency in Sandy, UT, before relocating to Fort Collins, CO, to co-found The Wholeness Center. Dr. Rondeau has developed a unique human brain database (EEGDataHub.com) capable of sorting QEEG markers used to evaluate responses to various treatments. Dr. Rondeau is President of Axon EEG Solutions (axoneegsolutions.com). When not writing, he researches and presents on EEG patterns in numerous arenas, as well as the integration of QEEG into psychotherapy and medical practice.