Screen Addiction – Biomarkers, Developmental Damage and Recovery

Stoyan R. Vezenkov and Violeta R. Manolova

Center for applied neuroscience Vezenkov, BG-1582, Sofia, e-mail: info@vezenkov.com

For citation: Vezenkov, S.R., Manolova, V.R. (2025) Screen Addiction – Biomarkers, Developmental Damage and Recovery. Nootism 1(1), 6-18, ISSN 3033-1765

*This paper was presented by Dr. Stoyan Vezenkov at the First Science Conference "Screen Children" on November 26, 2022, in Sofia, Bulgaria. The report was revised and updated as of March 2025.

Abstract

Screen addiction is a far more widespread issue than most researchers acknowledge or publish, surpassing even the alarming data that has emerged in the years following the COVID-19 pandemic. This underestimation stems, on the one hand, from the self-imposed limitations of screening procedures and, on the other, from the comorbidity of screen addiction with numerous severe disorders—such as Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), and Oppositional Defiant Disorder (ODD) etc.—in which screen addiction is rarely examined as a primary factor in their etiology. Instead, it is often treated as a consequence of these conditions or as an independent phenomenon, rather than an underlying cause.

Most studies on internet and gaming addiction focus primarily on digital content, failing to recognize that addiction can develop regardless of the content or screen device used—whether it be TV, smartphones, tablets, computers, or virtual reality (VR) systems. Our research demonstrates that screen addiction, regardless of its terminology (digital, internet, media, gaming, technological addiction, etc.), must be examined holistically by considering three critical factors: (1) The age of onset; (2) Sex differences; (3) Duration of addiction (how long it has been present). Only through a comprehensive evaluation of these factors could researchers begin to identify objective biomarkers of screen addiction. Without this approach, all machine-learning and deep-learning analyses risk becoming contaminated, superficial, and fragmented, failing to capture the true genesis of the problem. Consequently, therapeutic interventions will be significantly less effective.

This study presents individual cases in which biomarkers of screen addiction significantly deviate from those previously reported in the literature, offering new insights for a deeper understanding of the condition, while taking into account the three factors described above.

Our observations have revealed several common neurophysiological patterns associated with screen addiction:

Altered brain activity, characterized by either slowing (theta and/or alpha peaks) or acceleration (beta1 and beta2 bursts) during and after screen exposure.
Reversed hemispheric asymmetry, with dominant alpha and/or theta rhythms in the left hemisphere.
Significant amplitude increases in alpha and/or theta peaks when the eyes are closed.
Functional fragmentation—a term we introduced—where each recording site displays its own dominant frequency and distinct spectral parameters, observed in cases with onset at 0–2 years.
High local coherence but low transcortical coherence, indicating disrupted neural connectivity, specifically in early screen addiction with onset at 0–2 years.
Autonomic dysregulation, with the cortex appearing to "fall asleep" during screen use.

These biomarkers varied based on the age of onset, sex, and duration of screen addiction, underscoring its complex and individualized impact on the brain. A particularly significant conclusion from our findings is that the dominant frequency in a given brain region at the onset of screen addiction becomes imprinted and persists into later developmental stages. This pattern could serve as an objective predictor for determining the age of onset.

To further our investigation, we empirically discovered and introduced the Screen-Induced Pathological Vestibular Reflex Test (SIPVR-T)—a highly sensitive tool that correlates with the severity of early screen addiction up to the age of 12.

Our therapeutic approach provided strong evidence supporting these findings: We treat screen addiction and observe changes in biomarkers and symptoms—those that disappear were caused by screen addiction, while those that persist are unrelated.

This cause-and-effect distinction further validates our findings and underscores the urgent need for targeted treatment and preventive strategies to address screen addiction effectively.

Keywords: screen addiction, biomarkers, qEEG, functional fragmentation, HRV, complex therapy, biofeedback, Screen-Induced Pathological Vestibular Reflex

Introduction

Screen addiction, encompassing internet, technology, television, media, gaming, and social media addiction, has become a critical area of research in the fields of neuroscience, psychology, and public health. Excessive engagement with digital devices has been linked to neurophysiological alterations, cognitive impairments, and psychological distress. Only Internet Gaming Disorder (IGD) has been officially recognized and included in the appendix of the DSM-5 (American Psychiatric Association, 2013). However, it has been widely argued that Internet addiction extends beyond gaming and encompasses other problematic online behaviors, such as cybersex, online relationships, compulsive shopping, and excessive information searching, all of which pose significant risks for the development of addictive behavior. Neuropsychological research has shown that deficits in prefrontal executive control functions are closely linked to symptoms of Internet addiction, supporting theoretical models on its development and persistence. Control processes are notably diminished in individuals with Internet addiction when exposed to Internet-related cues, which can impair working memory performance and decision-making abilities. Consistent with this, functional neuroimaging and neuropsychological studies highlight cue-reactivity, craving, and impaired decision-making as key mechanisms underlying Internet addiction. The observed reductions in executive control align with findings in other behavioral addictions, such as pathological gambling. These findings further support classifying Internet addiction as a true addiction, given its significant similarities to substance dependence (Brand et al., 2016). With similar arguments, in the 11th revision of the International Classification of Diseases (ICD-11) published in 2022, only Gaming Disorder (GD) was introduced as a “Disorder due to Addictive Behaviours” by the World Health Organization (WHO).

One of the overarching goals of the emerging field of neuroscience and precision psychiatry is to incorporate advanced technologies to provide an objective data-driven personalized approach to the diagnosis and treatment of mental disorders. (Carvalho et al., 2020) Over the last years the field has witnessed a remarkable increase in interest on biomarkers for mental disorders. (Pinto et al., 2017)

Research suggests that internet and gaming addiction share neurophysiological similarities with substance use disorders, particularly in terms of cognitive control deficits, impulsivity, and altered neural activity patterns. Electroencephalography (EEG) studies have provided valuable insights into the neurophysiological mechanisms underlying these behavioral addictions, revealing alterations in cortical activity, connectivity, and coherence across different frequency bands (Balconi et al., 2014; Kuss et al., 2018; Blum et al., 2022).

Studies using qEEG have shown that individuals with internet and gaming addiction tend to exhibit increased theta and delta power in frontal and central regions, a pattern associated with reduced executive function and impulse control (Dong et al., 2014). Conversely, excessive engagement with social media and television has been linked to increased beta activity, which is often associated with hyper-arousal and cognitive overload (Weinstein et al., 2017). These findings suggest that different forms of screen addiction may present distinct yet overlapping qEEG endophenotypes, necessitating further investigation to refine diagnostic criteria and therapeutic interventions.

Male patients with gaming disorder of age 25 who experienced comorbid depression and anxiety symptoms had increased resting-state Delta and Theta activity. Delta-band activity normalized after 6 months of pharmacotherapy. The findings suggest that slow wave activity may be a neurophysiological marker with changes in addiction symptoms following treatments. (Kim et al. 2017; Burleigh et al., 2020)

Another notable finding in qEEG studies is coherence and functional connectivity abnormalities in individuals with screen addiction. Excessive screen use has been correlated with dysregulated frontal-parietal and fronto-limbic connectivity, potentially contributing to emotional dysregulation, attentional deficits, and reward-processing abnormalities (Lee et al., 2019; Park et al. 2017; Youh et al. 2017).

Furthermore, studies suggest that screen-related addiction may disrupt the balance between excitation and inhibition in neural circuits, particularly within the dopaminergic and GABAergic systems (Park et al., 2018). This imbalance has been implicated in reward deficiency syndrome (RDS), a condition in which individuals compulsively seek digital stimuli to compensate for underlying neural dysregulation (Blum et al., 2011; 2022).

Early-life exposure to screen devices appears to be particularly detrimental, as young children and adolescents exhibit greater neuroplasticity, making them more susceptible to the negative effects of excessive screen use on brain development (Dresp-Langley & Hutt, 2022). qEEG findings suggest that early childhood screen addiction is often associated with increased slow-wave activity (theta and delta) and reduced fast-wave activity (beta and gamma), reflecting cognitive underarousal and potential impairments in language development, executive functioning, and social cognition (Zhou et al., 2019). These findings align with clinical observations of pseudo-autism, ADHD-like symptoms, and emotional dysregulation in children with excessive screen exposure (Wang et al., 2021).

Given the pervasive impact of screen addiction on brain function, it is crucial to integrate qEEG findings into comprehensive diagnostic and intervention frameworks. Understanding the distinct qEEG signatures of different types of screen addiction can help refine treatment approaches.

Understanding how brain functional connectivity develops during early childhood is crucial for identifying key milestones in cognitive and neural maturation. Recent studies have revealed that functional connectivity undergoes significant changes during the first six years of life, with notable sex differences in developmental trajectories (Chen et al., 2021).

Heatmap analyses of brain connectivity patterns indicate that both males and females experience widespread changes in early childhood, but their rates of change differ. During the first year of life, females exhibit more pronounced connectivity changes, suggesting a faster initial maturation. However, by the second year, these changes become less pronounced in females, while males continue to develop at a steadier rate. Beyond the age of two, both sexes follow similar developmental trajectories, aligning with overall group trends (Chen et al., 2021).

These findings suggest that sex-specific differences play a role in early brain maturation, with females showing more rapid initial changes but stabilizing earlier than males. Given these developmental patterns, it is reasonable to divide age groups into 0–2 and 2–6 years, as the most significant sex differences occur between 0–1 and 1–2 years. (Chen et al., 2021).

From the vast number of studies attempting to identify biomarkers for different types of screen addiction—gaming, internet use, gambling, pornography, social media, shopping, and television—it can be summarized that there are no definitive differential characteristics in qEEG parameters that apply universally. Most meta-analyses include various age groups, even when focusing on a single gender and a specific type of screen addiction. They compare these groups with "control groups" that are classified based on subjective criteria using subjective scales, which is nearly impossible in modern research.

Moreover, the fact is often overlooked that a person may not perceive themselves as addicted according to standardized scales but may still exhibit addicted functioning—though not toward the studied type of addiction, but rather another related one. The same applies to individuals who have experienced a similar addiction in the past, which has left an "imprint" on their cortical physiology and has "migrated" into a different form of dependent behavior, rather than the one under investigation. This contaminates control groups with individuals who, based on subjective indicators, do not appear addicted to the specific type of screen addiction being studied but are, in fact, dependent on another related form.

The aim of this study is to introduce a new approach to researching screen addiction and its various forms—namely, through a comprehensive individual therapy that uncovers the deeper layers of addisted functioning. This type of functioning is far more widespread than questionnaire-based assessments can capture, as they primarily focus on behavior.

Screen-related addicted functioning is not only more prevalent but also much more deeply ingrained than we might assume. It manifests through a wide spectrum of symptoms, similar to those conceptualized by Blum in his introduction of the term Reward Deficiency Syndrome. (Blum et al., 2011, 2022)

This report compiles and summarizes data from hundreds of cases treated at the Center, where complex individual neurotherapy (involving biofeedback and EEG neurofeedback) has revealed that screen addiction is at the root of numerous syndromes and functional disorders for which people seek our help. Once the addiction is treated, these conditions completely disappear.

This approach is the exact opposite of the conventional one. The sample does not consist of individuals who initially reported screen addiction as their primary complaint or were diagnosed as such by a psychiatrist. Instead, throughout the course of therapy, it becomes evident that screen addiction is at the core of their suffering and symptomatology.

Similarly, the biomarkers identified through years of therapeutic programs at the Center have been empirically derived using a specific criterion—observing which changes in autonomic and cortical physiology correspond with the disappearance of symptoms. In other words, if treating screen addiction leads to the resolution of symptoms, then screen addiction is the root cause, rather than another condition.

It is clear why such an approach does not align with the standards of conventional neuroscience. However, modern science needs a more humane perspective—one that prioritizes genuine healing over the mere classification of individuals based on statistically significant or insignificant differences in parameters that disregard their personal history, lifestyle, and value system.

Design

This study follows a quasi-experimental pre-post intervention design with an observational component. All participants underwent treatment for screen addiction, during which changes in biomarkers and symptoms were closely monitored. Symptoms that disappeared after the intervention were attributed to screen addiction, while those that persisted were considered unrelated to it.

A control group was not included, as all clients at the Center were treated individually and as soon as possible to alleviate their symptoms and conditions. Given the urgent nature of these cases, delaying treatment for research purposes was not ethically or clinically justifiable. Instead, the study relied on pre-post symptom resolution patterns to infer causality. Additionally, a longitudinal observational approach was used to assess the progression of symptom changes over time, allowing for a differentiation between screen-related and unrelated conditions.

The data and conclusions were drawn based on the individual therapies conducted and summarized according to their success rates. Cases that were not completed or were discontinued for various reasons, which are not discussed here, were excluded from the study. The average success rate was 550 out of 831 cases (66.2%).

Methods

Participants and Study Conditions

All individuals and children participating in this study were examined at the Center under identical conditions, using the same functional assessment equipment and treated according to an own unique (know-how) methodology developed at the Center.

Table 1 summarizing all participants including in this study, categorized by age group and sex.

Age Group	Total Participants	Boys/Males (n, %)	Girls/Females (n, %)
Children (3–6 years)	141	101 (71.6%)	40 (28.4%)
Children (6–12 years)	85	60 (70.6%)	25 (29.4%)
Adolescents (12–18 years)	74	49 (66.2%)	25 (33.8%)
Adults (18–35 years)	152	63 (34.9%)	99 (65.1%)
Adults (35+ years)	98	35 (35.7%)	63 (64.3%)
Total	550	308 (56%)	242 (44%)

Quantitative EEG (qEEG)

EEG recordings were conducted using a 19-channel monopolar montage, employing Neuron-Spectrum-4P hardware and Neuron-Spectrum.NET software, manufactured by Neurosoft LLC, Russia.

The quantitative spectral analysis, including the calculation of amplitudes across different brain rhythm bands and other quantitative parameters, was performed using Neuron-Spectrum.NET. Comparison with a neurodatabase for registered quantitative parameters (absolute and relative amplitude, coherence, phase lag, Z-scores, etc.) was conducted using NeuroGuide Deluxe 3.3.0 from Applied Neuroscience, Inc., USA.

EEG Recording in Children Under 12 Years

EEG recording was not performed on children who did not tolerate wearing an EEG cap.
In children not enrolled in a screen detox program, EEG was recorded during screen exposure (TV or smartphone).
In children already in a screen detox program who refused to wear the EEG cap, screen stimulation was not reintroduced to enable EEG recording.
No EEG recordings were performed on children who refused the cap with or without screen exposure, ensuring that no coercion was applied.

Autonomic Imbalance Assessment

In parallel with EEG recording, autonomic peripheral signals were measured, including: Heart rate (HR), Heart rate variability (HRV), Peripheral temperature, Respiratory rate, Skin conductance level (SCL), Electromyography (EMG).

These signals were recorded using 8-channel Gp8 Amp hardware and Alive Pioneer Plus software from Somatic Vision Inc., USA.

Analysis of various HRV parameters (HR, SDNN, Total power, LF/HF ratio, smoothness, stress index, SNS index, PNS index, etc.) was performed in Alive Pioneer Plus and further analyzed using Kubios HRV Scientific Lite.

Based on these assessments, autonomic imbalance was categorized into four distinct states: Sympathicotonia, Vagotonia, Co-activation, Co-relaxation.

Our Own Unique (Know-How) Testing Protocols

For all children under 12 years, a series of customized tests were conducted using the Center’s own unique (know-how) methodology, developed through clinical therapeutic experience. These tests were performed with or without instrumentation, under the conditions described above.

The tests assessed: Reactions to screen stimuli, Motor and vestibular maturity, Verbal and communication skills, Prosocial behavior, Impulse control, Attachment to parents, Compulsive behaviors, Fear responses.

Therapy Based on Our Own Unique (Know-How) Methodology

All children up to 18 years old underwent screen addiction therapy for a duration of 6 to 9 months. The treatment was combined with biofeedback therapy for both parents, aiming to restore their autonomic, affective, cognitive, and psychosocial balance. The goal was to establish a secure attachment style, enabling parents to adequately support their children through weekly developmental changes.

For children under 12 years, therapy was non-instrumental and included: Complete screen detox, Sensorimotor reset, Restoration of autonomic balance, "Cortical awakening", Gradual restoration of attachment—first to the therapist, then progressively to parents.

Results

Adults Over 35 Years

There is a noticeable age threshold around 35 years, beyond which the biomarkers for screen addiction change significantly. In this age group (98 cases, 63 females and 35 males), most cases (36) exhibit low alpha amplitude, even with closed eyes, alongside elevated beta1, beta2, and beta3 amplitudes across most recording sites.

The symptomatology includes anxiety and/or depression, as well as a full spectrum of sleep-related disturbances (beta-type attention deficit, irritability, low energy levels, autonomic imbalance characterized by pronounced vagotonia, and freeze responses). These disturbances are evident both in terms of quantity and quality.

This group exhibits clear gender differentiation:

Among men, the insecure-avoidant attachment style is predominant, leading to difficulties in forming and maintaining healthy relationships. This often results in either avoiding family commitments or dismantling existing relationships, frequently ending in divorce and emotionally distant, avoidant relationships with their children. The most common subtypes of screen addiction in men are gaming and pornography.
Among women, the insecure-anxious attachment style is more prevalent. This manifests as excessive control in relationships with spouses and children, overprotectiveness, yet combined with emotional unavailability. Additionally, many women exhibit workaholism and/or the "perfect housewife" syndrome. The most common subtype of screen addiction among women in this age subgroup includes watching TV series, following podcasts on specific topics—often esoteric or astrological content—and spending time on social media, primarily as passive consumers rather than active participants. Additionally, online shopping and consuming content with no specific focus are also prevalent. Unlike younger groups, social isolation and alienation were less pronounced in this population. However, monotony and stereotypical behavior were commonly observed in most participants.

Women predominated in this group at a 2:1 ratio, though this may be attributed to their higher likelihood of seeking professional help.

Adults Aged 18–35

The most affected group, which displayed a significantly different biomarker profile, consisted of adults aged 18 to 35 years (152 cases: 99 females (65.1%) and 63 males (34.9%)).

Among men (51 cases, 81% of all males), screen addiction primarily involved television and the internet, often combined in varying proportions with gaming, pornography, gambling, or unspecific internet content (12 cases, 19% of males).

Among women (82 cases, 82.8% of all females), the predominant addictions were television and the internet, with a focus on social media and online shopping.

This group exhibited alpha1 peaks, sometimes accompanied by simultaneous alpha2 and/or 12–13 Hz rhythms during screen activity or even at rest with open eyes. Upon closing the eyes, these rhythms appeared with significantly increased amplitudes (often exceeding 100 µV) in O1 recording sites, and occasionally in O2 recording sites. This was a clear indicator of an overstimulated visual cortex, often accompanied by headaches and photophobia.

Additionally, strong alpha peaks with much higher amplitudes appeared in left-central and/or parietal recording sites. A single alpha peak was typically observed in isolated recording sites, most commonly in C3. Depending on when screen addiction first developed, theta-alpha peaks (typically around 8 Hz) were observed alongside alpha peaks.

Two or three distinct patterns tended to alternate or persist simultaneously: (a) 9 Hz rhythms with decreasing amplitude from frontal to parietal recording sites, with the lowest amplitude in the visual cortex. (b) 11–12 Hz rhythms with very high amplitudes, localized in the occipital and/or parietal regions, often with inverted asymmetry (higher in the left hemisphere). These markers were observed both with open and closed eyes.

Alongside screen addiction, this group frequently experienced the following issues, prompting them to seek professional help:

Anxiety (40 females, 40.4% of females; 35 males, 55.6% of males) or depression (70 females, 70.7% of females; 20 males, 31.7% of males)
Sleep disturbances leading to alpha-type attention deficits and autonomic imbalance (65 females, 65.7% of females; 41 males, 65.1% of males)
Chronic fatigue (72 females, 72.7% of females; 38 males, 60.3% of males)
Postural muscle pain (85 females, 85.9% of females; 56 males, 88.9% of males)
Autonomic imbalance, which manifested in four forms:
- Sympathicotonia (39 females, 39.4% of females; 32 males, 50.8% of males)
- Vagotonia (65 females, 65.7% of females; 25 males, 39.7% of males)
- Co-activation (25 females, 25.3% of females; 18 males, 28.6% of males)
- Co-relaxation (15 females, 15.2% of females; 6 males, 9.5% of males)

These imbalances manifested as panic attacks, shortness of breath, rapid exhaustion, irritability, sudden outbursts, and avoidance of physical, emotional, or cognitive effort. Additionally, many exhibited emotional detachment, social withdrawal, and a preference for virtual interactions over real-life relationships. This isolation was driven by screen-based activities as a primary factor, rather than using screens as an escape from real-life difficulties.

For most individuals in this group:

Monotony and stereotypical behavior were common, often interspersed with episodes of risk-taking (85 females, 85.9% of females; 51 males, 81% of males). Their lifestyle tended to lack variation, with rigid routines and limited engagement in new experiences.
Sexual dysfunction, such as low libido, was reported in 120 cases (84 females, 84.8% of females; 36 males, 57.1% of males), while 85 cases (55.9% of the total sample) exhibited other sexual issues, such as: hypersexuality (25 females, 25.3% of females; 8 males, 12.7% of males) and premature ejaculation (45 males, 71.4% of males)
56 males (88.9% of males) and 61 females (61.6% of females) expressed reluctance to start a family, while 68 cases (44.7% of the total sample) explicitly did not wish to have children.
81 females (81.8% of females) and 39 males (61.9% of males) struggled to form meaningful relationships with the opposite sex.
35 females (35.3% of females) and 17 males (27% of males) were parents, and among them, 45 cases (29.6% of the total sample) had screen-addicted children with severe developmental impairments.

Five males in this group were diagnosed with ASD, all of whom were high-functioning, with screen addiction onset between the ages of 3 and 4.

Adolescents (Ages 12–18)

Among adolescents aged 12–18 years (74 cases: 25 girls (33.8%) and 49 boys (66.2%)), very high occipital alpha activity in O1 was observed in 70 cases (94.6%), both with open and closed eyes.

In 14 cases (18.9%), a neurologist prescribed anticonvulsant treatment due to high amplitudes in the visual cortex, photosensitivity, or seizures, with absence seizures in 8 cases (10.8%).
Additionally, 6 cases (8.1%) exhibited sleep-related seizures, typically occurring around two hours after falling asleep.

Higher Alpha Wave Amplitudes in Left Hemisphere Recording Sites

This group exhibited higher alpha wave amplitudes in left hemisphere recording sites, with the highest occurrences in: C3 (19 girls (76%), 38 boys (77.6%)); P3 (21 girls (84%), 42 boys (85.7%)); F3 (16 girls (64%), 32 boys (65.3%)); Fp1 (8 girls (32%), 19 boys (38.8%))

Other Observations

Episodes of theta peaks, both with open and closed eyes, or during screen activities (such as watching videos or gaming), were also noted.
71 cases (95.9%) exhibited fragmented slow-wave activity, with frequency differences greater than 1.5 Hz across different recording sites, and multiple alternating or simultaneous functional patterns.
Tics, facial grimaces, and various involuntary movements were reported in 32 cases (43.2%).

ASD Cases

There were 17 cases (23%) of individuals diagnosed with ASD (12 boys (24.5% of boys), 5 girls (20% of girls)), who exhibited a characteristic slow-wave activity imprint between 2 and 6 Hz. This was directly correlated with the onset of screen addiction between the ages of 9 months and 3 years.

This slow-wave activity was observed alongside high alpha or even beta1 peaks in certain recording sites, displaying multiple distinct functional patterns. A hallmark of these cases was the presence of infantile brain activity, corresponding to the developmental stage at which the addiction began.

Key ASD symptoms observed:

Very severe form of screen addiction, mostly to smartphones and internet (distinct content)
Primitive reflexes and severe language impairments, including complete nonverbalism (6 boys (12.2% of boys), 3 girls (12% of girls))
Symptoms typical of low-functioning ASD, such as:
- Monodietary habits
- Tantrums and severe meltdowns
- Complete social withdrawal and living in an isolated inner world
- Lack of interest in people and absence of eye contact
- Stereotypical behavior
- Fascination with a limited number of objects or activities
- Significant sleep disturbances, both quantitative and qualitative

Notably, in 5 cases (6.8%), deciduous teeth had not yet been replaced by permanent teeth, which was considered a sign of "preserved" or stalled development.

Children Aged 6–12 Years

Among children aged 6–12 years (85 cases, 60 boys and 25 girls), the following diagnoses were predominant:

Autism Spectrum Disorder (ASD) – 41 cases, 30 boys and 11 girls
Attention-Deficit/Hyperactivity Disorder (ADHD) – 20 cases, 15 boys and 5 girls
Oppositional Defiant Disorder (ODD) – 65 cases, 45 boys and 20 girls
Specific learning difficulties (SLD) – all 85 cases
In low-functioning, nonverbal children with ASD (35 cases, 29 boys and 6 girls), the predominant biomarkers included:
2–6 Hz peaks with open eyes and during screen activity, occurring simultaneously with multiple alpha peaks in different locations, most frequently in central or posterior recording sites.
Fragmented activity, meaning different dominant frequencies in different recording sites at any given moment.
Higher frequency (by more than 1.5 Hz) in parietal and occipital recording sites compared to central, frontal, and prefrontal regions.
Hyperactivity (HA) is typically theta subtype, with the theta/beta ratio significantly above normal.

Notably, in 30 cases (20 boys and 10 girls), slow-wave activity in the frontal cortex did not manifest as hyperactivity but rather as complete disengagement of the child.

The most severe cases were observed in children who were fed in front of a screen, highlighting the profound impact of screen exposure on their development.

Children Aged 3–6 Years

This age group was the most affected by screen addiction due to the COVID-19 pandemic. The prevalence of severe developmental disorders, such as language impairments, ASD, and ADHD, has been growing exponentially every year since the lockdowns, which resulted in young children being heavily exposed to screens.

Based on our observations, every third child in nurseries and kindergartens in Bulgaria has severe developmental issues (unpublished data). Among 141 children (101 boys (71.6%) and 40 girls (28.4%)) diagnosed with ASD, developmental delay (DD), ADHD, and severe language deficits, 139 children (98.6%) had severe screen addiction, while 2 girls (5% of girls, 1.4% of all children) were addicted to a limited selection of audio songs and fairy tales, as well as toys with lights and sounds.

EEG Findings (32 Children, 22.7%)

Among the 32 children who underwent EEG, the following patterns were observed:

Strong theta-alpha peaks in O1
Fragmented theta and alpha activity across different recording sites, with multiple cortical activation patterns
Simultaneous theta and alpha peaks occurring in the same sites
Atypically high alpha wave amplitudes for this age, but only in certain sites, mostly C3

In 25 children (78.1% of EEG cases, 17.7% of all children), the EEG was performed during screen exposure (watching a favorite song or video). The content held their attention so strongly that they did not react to their surroundings.

Screen-Based Behavioral Conditioning

Using screens to keep a child engaged was commonly reported for: Feeding, Toilet training, Sitting still in social settings, Car rides, Other daily activities.

Screen exposure temporarily calmed the child, after which they fell into one of two subgroups:

Hyperactive after screen use (81 boys (80.2% of boys), 15 girls (37.5% of girls))
Completely disengaged (20 boys (19.8% of boys), 25 girls (62.5% of girls)), absorbed in their own world.

For both groups, compulsively repeated stereotypical behaviors that mimicked screen content included:

Spinning objects (e.g., toy car wheels, spinning tops, etc.) or watching spinning objects (e.g., a running washing machine, etc.)
Turning the head left and right repeatedly
Staring fixedly at a single point from an angle
"Flapping" fingers in front of the face
Rapid switching between objects, most often brightly colored toys, within the visual field
Playing with flashing or sound-producing toys
Putting objects in the mouth
Quickly moving colorful pictures in front of the eyes
Arranging puzzles
Lining up numbers, letters, or alphabet characters in sequence
Arranging objects, usually toys, in a specific order, such as lining up toy cars
Listening to the same sounds or songs repeatedly on devices

The ratio between these two groups was 2:1, meaning every third child was entirely disconnected, mostly girls, displaying stereotypical behavior that could only be interrupted by aggression, self-harm, tantrums, or oppositional defiant behavior.

Sensory Processing Disruptions

A hyperstimulated visual and auditory cortex became the primary pathway for craving and sensory stimulation, while other sensory systems were partially impaired, including: Taste, Smell, Touch, Balance (vestibular system), Proprioception and nociception (pain perception), Thermoregulation.

In 95% of cases (134 out of 141 children), children panicked or became hysterical when their eyes were covered or ears were plugged, demonstrating an extreme dependence on visual and auditory stimuli linked to screen addiction.

The fragmented EEG cortical patterns correlated with hypersensitivity in certain body parts (for example toes, head, eyes, ears) and hyposensitivity in others (for example arms, fingers, back), indicating sensory system maturation delays.

Overstimulated but underdeveloped visual and auditory systems led to impairments such as:
- Sleep Disorders (135, 95,7%)
  - Difficulty falling asleep or needing a specific routine
  - Waking up with crying and screaming
  - Waking up and remaining awake for extended periods
  - Sleeping with one parent and being unable to sleep alone
  - Inability to sleep in the dark
  - Other related disturbances
- Lack of eye contact (106 children, 75.2%)
- Lazy eye (Amblyopia) (123 children, 87.2%)
Eating disorders (135 cases, 95.7%), including:
- Monodietary habits
- Chewing difficulties and refusal to eat solid food
- Avoidance of new or visually different foods
- Carbohydrate diet (107 cases, 78.7%)
- Metabolic issues (75 cases, 53.2%), such as heavy metal accumulation and other imbalances
- Еat objects/non-edible substances such as mortar, paper, wood, dry leaves, etc.
Elimination problems (124 cases, 87.9%), including:
- Wearing diapers accompanied by encopresis and fecal incontinence (115 cases, 81.6%)
- Wearing diapers due to enuresis and urinary incontinence (95 cases, 67.4%)
- Chronic constipation (79 cases, 56%)
Motor skill impairments were observed in both gross and fine motor functions:
- Gross motor impairments
  - Walking without crawling first (115 children, 81.6%)
  - Toe walking (81 children, 57.4%)
  - Unsteady gait (94 children, 66.7%)
- Fine motor underdevelopment (120 children, 85.1%)

These findings highlight widespread deficits in sensory-motor integration and autonomous dysfunctions, with significant functional impairments affecting movement coordination, sensory processing and autonomic regulation.

Screen-induced Pathological Vestibular Reflex (SIPVR)

A balance test developed at our Center was a highly sensitive empirical tool for detecting screen addiction. The test involves lifting the child upside down without support.

In 99% of cases (up to age 12, possibly beyond), the test was positive, triggering reactions ranging from mild discomfort to severe activation of a primitive reflex, which we termed Screen-Induced Pathological Vestibular Reflex (SIPVR). These responses included:

Arching backward
Reaching out to grasp onto something
Trembling and panic
Full-body muscular stupor, as the child desperately seeks support
Fear of falling

The reflex weakened when the child grasped onto something. We used the SIPVR test in various modifications to evaluate recovery progress.

Even after months of therapy and the disappearance of the reflex, if a child relapsed into screen exposure, the SIPVR reappeared immediately, confirming that screen stimulation disrupts and alters the balance system. The severity of these regressions, caused by reintroducing screens into the child's life, will be discussed separately.

By identifying a re-emerging positive SIPVR, we could determine whether parents or institutions were truly following the prescribed screen detox program or deliberately deceiving about its implementation, which was not uncommon (46 cases, 32.6%). When the reflex does not disappear (22 cases, 15.6%) during therapy, it serves as a reliable marker that screen detox is not being implemented, either with or without the parents' knowledge, and that screen stimulation is continuing.

Discussions

There is one common characteristic of the impact of screen addiction on child and adult development—once it appears, it leaves a lasting imprint on the stage of development at which the child or individual is at that moment. Most likely, this occurs due to high reinforcement of the activity, but not just from the stimulation itself, but from everything associated with it during the time of engagement. This includes both external conditioned stimuli and internal states related to the child’s developmental stage, encompassing all characteristic reflexes, drives, emotions, and behaviors typical of that period of addicted functioning.

Perhaps this is why screen addiction acts as a "developmental anchor"—regardless of the progress observed in a child’s development, screen exposure triggers a complete regression to the states and behaviors characteristic of the earliest stages of addiction development. For this reason, screen addiction is not just a disorder, but a developmental disruption, with a wide spectrum of symptoms that make it impossible to define universal biomarkers. Every child and individual halts their development at a different point in their unique ontogeny, making it difficult to identify repetitive markers.

Paradoxically, despite everything stated so far, all screen-addicted children with early screen exposure (0–2 years) exhibit clearly defined markers that even an untrained specialist can immediately recognize today—lack of eye contact, lack of interest in people, stereotypical compulsive behaviors, specific obsessions, tantrums, and hysterical outbursts in response to change, among others. In other words, although the symptomatology forms a broad spectrum, there are many distinct signs that harm development and led to the creation of the ASD diagnosis, which is entirely unnecessary and burdened with hopelessness. The path to recovery is difficult but not impossible, though this will be explored in other studies.

Another characteristic feature of screen addiction is its negative correlation with speech and language development (Madigan et al., 2020; Jing et al., 2023; Barr, 2019; DeLoashe et al., 2010; Strouse et al., 2021)[1], which serve as the foundation for intelligence and the sense of self and others, forming the starting point for personality development.

This phenomenon is observed regardless of the age at which screen addiction occurs, but it manifests differently depending on the developmental stage at which it strikes. For example:

In the youngest children, it results in a complete lack of speech or delayed speech development.
In children aged 3–6 years, it leads to regression in existing language skills, automatic speech without adequate communication in a social context and intentionality, difficulties in developing social context, and delays or deficits in cognitive development.
In older children up to 18 years, it manifests as attention deficits, regression in cognitive functioning, and a loss of interest in academic progress.
In adults, even those with high intelligence, it is associated with stereotypical and dogmatic thinking ("more of the same"), rigidity in progressing to the next stage of development, and a lack of or difficulty in self-actualization.

Across all age groups, screen-addicted individuals appear to lack the need to communicate with their peers, leading to the erosion of fundamental human evolutionary achievements such as language, thought, and self-awareness. Screen addiction seems to compete with and counteract language development, as well as the cognitive and personal growth that builds upon it.

This phenomenon is likely true for all forms of addiction, but screen addiction presents a unique opportunity: because we are now witnessing such large-scale addiction in children, we can, for the first time, gain deeper insight into this fundamental aspect of human nature and development.

For children under the age of 6, the consequences are severe developmental disorders, including ASD, ADHD, and generalized developmental disorders. Parents typically report that their child developed normally until around one year to a year and a few months (with the exception of children exposed to screens as infants, and in some cases, immediately after birth!). At some point, the child lost eye contact, withdrew into their own world, and it seemed as if "something replaced their child."

In searching for a cause, parents often attribute this regression to vaccinations, medical interventions, accidents, falls, and various other factors. Rarely, and only after extensive searching, do they come across information about early screen addiction, at which point they gradually begin to understand what happened to their children.

The Impact of Screen Addiction Across Developmental Stages in Both Sexes

We observed a 2:1 ratio of boys up to age 6 of developing screen addiction compared to girls, indicating greater vulnerability in boys during early childhood. It is highly likely that the more extensive brain changes in boys during the second year—when screen exposure is typically introduced—make them more vulnerable to developing screen addiction at this age. Conversely, since girls undergo significantly fewer changes during the second year, they may be more resistant to developmental disruptions caused by early screen exposure (Chen, 2021).

The 2D:4D ratio refers to the ratio between the second digit (index finger, 2D) and the fourth digit (ring finger, 4D). A lower 2D:4D ratio means that the ring finger (4D) is relatively longer than the index finger (2D), which is often considered an indicator of higher prenatal testosterone exposure and lower prenatal estrogen exposure. The findings suggest that individuals with a lower right-hand 2D:4D ratio—and therefore higher prenatal testosterone levels—may have a greater tendency toward Internet Gaming Disorder (IGD). This aligns with prior research linking prenatal hormone exposure to risk-taking, impulsivity, and reward-related behaviors, all of which are relevant to IGD. (Kornhuber et al., 2013; Müller et al., 2017; Canan et al., 2017)

With equal levels of screen exposure, the severity of its effects is significantly higher in boys than in girls at the youngest ages. This difference is likely due to variations in developmental intensity between the sexes during the first two years of life. Girls experience a rapid developmental surge in the first year, followed by a slower progression in the second year, whereas boys develop at a more consistent rate across both years.

When screen addiction onsets before the age of one, its impact is equally severe for both boys and girls. However, if it emerges during their second year, the damage tends to be more pronounced in boys. The most common period for first screen exposure and early screen addiction falls between the first and second years, which explains why boys are more affected. This early exposure leaves a lasting imprint on overall development and can shape future cognitive and behavioral outcomes.

In typical development, boys and girls go through distinct sensitive periods and developmental leaps. If a study is conducted during any of these sensitive periods, when boys and girls of the same chronological age are at different developmental stages, the impact of screen addiction will vary significantly.

For instance, between ages 3 and 6, developmental intensity between boys and girls evens out. However, during puberty, girls experience a temporary developmental lead in the first half, followed by a slowdown in the second half, until they equalize with boys around age 18. After age 18, males begin to develop more rapidly than females, but by the time the prefrontal cortex fully matures at around 25–26 years, the rate of development evens out again.

Beyond this stage, further developmental progress is no longer driven by brain maturation but rather by personality development and lifestyle choices. This explains why screen addiction primarily leads to psychological, behavioral, and personality changes, while physical consequences emerge as secondary effects.

By the age of 25, when the prefrontal cortex fully matures—the center of self-actualization and value system formation—screen addiction, like any other addiction, directly affects brain function and the nervous system.

A similar pattern is observed in the prevalence of early screen addiction, which occurs at a 2:1 ratio in favor of boys. However, this ratio gradually decreases with age, equalizing during puberty and even reversing in favor of females in later adulthood. After puberty, gender differences in screen addiction become content-specific—whereas before puberty, the type of screen activity plays little to no role in its impact.

qEEG Biomarkers and Age-Specific Vulnerability

Our analysis of biomarkers and qEEG parameters reveals clear correlations between brain activity patterns and the severity of screen addiction across different age groups, regardless of gender. However, the degree of severity is strongly influenced by sex at specific developmental stages:

Boys are more vulnerable in early childhood, experiencing more severe effects of screen addiction.
During puberty, the differences equalize, meaning both sexes are affected similarly.
After the age of 35, the trend reverses, with women showing greater vulnerability to screen-related disruptions.

qEEG Findings and Trends

qEEG analysis of cortical activity revealed distinct patterns based on the age of onset:

The earlier screen addiction begins, the slower the dominant brain waves in qEEG, making it possible to estimate the approximate age at which dependency first formed.
Earlier onset is also associated with more fragmented cortical activity, meaning that different brain regions exhibit varying dominant frequencies and spectral characteristics. This finding is consistent with research showing higher local connectivity and lower trans-cortical coherence (Burleigh et al., 2020).

For individuals aged puberty to 35 years, screen addiction leads to two major patterns of qEEG changes:

The emergence of alpha peaks with open eyes in left-hemisphere recording sites, increased “drowsiness”, especially under cognitive load, and strong occipital peaks in O1.
The appearance of beta1 and beta2 bursts in various brain regions.

Normally, alpha peaks appear in a resting cortex, indicating drowsiness, and are not typically present under cognitive load. However, in screen-addicted individuals, the alpha peaks we recorded during screen exposure persisted even in other conditions and while performing tasks. Although persistent alpha peaks are often considered a biomarker for the alpha subtype of ADD, in most of our cases, they served as a marker for screen addiction. Notably, these abnormal alpha peaks diminished following screen addiction therapy, confirming their association with screen dependency rather than an inherent attention disorder.

In individuals who develop screen addiction after the age of 35, the brain shows a different response:

Alpha peak amplitudes decrease significantly, even with closed eyes.
Beta1 and beta2 bursts become more frequent, correlating with chronic insomnia, anxiety, chronic fatigue, burnout, and subsequent phases of exhaustion and depression.

Deviations from Typical Age-Related Trends

While these age-dependent trends are well-established, some individuals exhibit characteristics of other age groups, as environmental and lifestyle factors play a crucial role in shaping the brain’s response to screen addiction.

Deviations from these general trends typically fall into two categories:

Premature aging, where cognitive and neurological decline occurs earlier than expected.
Infantilization, where brain function remains developmentally immature.

Both chronic stress, traumatic experiences, and environmental toxicity act as powerful accelerators of both aging and infantilization, significantly intensifying deviations from typical developmental trajectories.

Content specific screen addiction in both sexes

Our results show that after puberty, the type of content determines the specific subtype of screen addiction that each sex is more likely to develop. Before puberty, content plays no role and is instead influenced by initial exposure and the available options for the child, such as television, smartphones, the internet, videos, or gaming.

Whereas online shopping (Rose et al., 2014) and online social networking channels (Montag et al., 2015) are more associated with being female, overusage of platforms being related to pornography (Carroll et al., 2008), Internet gaming (Montag et al., 2011), or online gambling (Griffiths et al., 2008) are more a domain of males. These are the big five Internet content category. Another study confirmed that women have been more likely to use the Internet for social networking and streaming videos than men have. Compared to women, a higher proportion of men report Internet gaming as their most frequent Internet activity (Canan et al., 2017).

Our research demonstrates that screen addiction, regardless of its terminology (digital, internet, media, gaming, technological addiction, etc.), must be examined holistically by considering three critical factors: (1) The age of onset; (2) Gender differences; (3) Duration of addiction (how long it has been present).

Only through a comprehensive evaluation of these factors can researchers begin to identify objective biomarkers of screen addiction. Without this approach, all machine-learning and deep-learning analyses risk becoming contaminated, superficial, and fragmented, failing to capture the true genesis of the problem. Consequently, therapeutic interventions will be significantly less effective.

Conclusions

This study provides new insights into screen addiction by analyzing individual cases where biomarkers significantly deviate from those previously reported in the literature. By considering age of onset, sex, and duration of addiction, our findings highlight the complex and individualized impact of screen addiction on brain function.

Our observations identified several consistent neurophysiological patterns in individuals with screen addiction:

Altered brain activity, characterized by slowing (theta and/or alpha peaks) in cases with onset before age 12 and acceleration (beta1 and beta2 bursts) in cases with onset after age 35, observed both during and after screen exposure.
Reversed hemispheric asymmetry, with dominant alpha and/or theta rhythms in the left hemisphere.
Significant increases in alpha and/or theta amplitude when the eyes are closed.
Functional fragmentation—a term we introduced—where each brain region exhibits its own dominant frequency and spectral parameters, specifically in cases where screen addiction begins between 0–2 years.
High local coherence but low transcortical coherence, indicating disrupted neural connectivity in early screen addiction (onset at 0–2 years).
Autonomic dysregulation, with the cortex appearing to "fall asleep" during screen use.

A particularly significant conclusion from our findings is that the dominant frequency in a given brain region at the onset of screen addiction becomes imprinted and persists into later developmental stages. This suggests that screen addiction leaves a lasting neurophysiological signature, which could serve as an objective predictor of both the age of onset and the severity of the condition.

Moreover, our study confirms gender-related differences in screen addiction vulnerability. Boys are more susceptible in early childhood, whereas sex differences equalize during puberty and later reverse in favor of females after age 35. Additionally, after puberty, the content of screen exposure determines the specific subtype of addiction, whereas before puberty, the type of content is irrelevant, and addiction depends solely on exposure and accessibility.

To enhance our ability to assess and diagnose early screen addiction, we empirically discovered and introduced the Screen-Induced Pathological Vestibular Reflex Test (SIPVR-T)—a highly sensitive tool that correlates with the severity of early screen addiction up to the age of 12.

These findings highlight the urgent need for early detection, intervention, and prevention strategies to mitigate the long-term neurological and developmental consequences of screen addiction.

Acknowledgments

We extend our deepest gratitude to all the individuals and families who participated in this study, trusting us with their experiences and allowing us to explore the impact of screen addiction on neurophysiological development.

We also acknowledge the dedicated team of researchers, clinicians, and therapists at our Center, whose expertise, commitment, and continuous efforts made this research possible.

This research is dedicated to all those striving to understand and address the growing challenges of screen addiction, with the hope that our findings contribute to more effective prevention, diagnosis, and intervention strategies.

[1] The sources are far more extensive for all the topics discussed, and this report does not claim to provide an exhaustive bibliographic reference

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19.03.2025

issue01-April2025