Neurophysiologic Reactions during Heart Rate Variability Biofeedback Session in Adolescents with Different Risk of Internet Addiction

Демин, Д. Б.; Поскотинова, Л. В.

doi:10.3390/ijerph19052759

Cited by 5 publications

(3 citation statements)

References 28 publications

(46 reference statements)

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“…People with low levels of executive functioning are vulnerable to intermittent reinforcement principles connected to internet-related activities. These processes are key factors in the transition from voluntary/goal-directed actions (with an appraisal of action consequences) to habitual actions (seemingly automatic and uncontrolled), which is at the basis of addictive behaviors [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. Another model, the Interaction of Person-Affect-Cognition-Execution (I-PACE), proposes that PIU behaviors may be explained by looking at interactions between predisposing factors (e.g., impulsivity, anxiety, depression, general distress), moderators (e.g., coping style, self-regulatory capacities, and internet-related attentional and cognitive biases), and mediators such as reduced inhibitory control in combination with reduced executive functioning and diminished decision making [ 39 , 40 , 41 ].…”

Section: Introductionmentioning

confidence: 99%

Problematic Smartphone Use Leads to Behavioral and Cognitive Self-Control Deficits

Fabio

Stracuzzi

Faro

2022

IJERPH

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Excessive use of smartphones has been associated with a number of negative consequences for individuals. Some of these consequences relate to many symptoms of behavioral addiction. The present study aims to investigate whether participants with high levels of smartphone usage may have difficulty with their ability to wield the self-control that is needed to restrict smartphone usage compared to participants with lower levels of smartphone addiction. Specifically, we expect that people with high levels of smartphone usage may have problems in refraining from using a smartphone. In addition, we expect people with a high level of smartphone use may show deficiencies in cognitive tasks such as memory, executive control, and visual and auditory attention. An ABA design was applied to analyze the effects of smartphone withdrawal. The first A refers to baseline measurements: Visual RT, Auditory RT, Go/No-Go RT and N-Back RT and Eriksen flanker RT. The B refers to 3 days of smartphone withdrawal, whereas the second A refers to the same measurements used in the baseline. In addition, several standardized scales were administered, among them: Smartphone addiction scale-short version (SAS-SV), Fear of missing out scale (FoMOs), Procrastination scale, and Psychological General Well-Being Index. One hundred and eleven participants took part in the study. Based on median split they were divided into two groups: high level and low level smartphone users. Moreover, thanks to an app installed on the participants’ smartphones, it was possible to measure levels of compliance with the task. Results indicate that participants with low levels of smartphone usage show less difficulty in their ability to wield the self-control needed to withdraw smartphone use and faster reaction times on cognitive tests than participants with high levels of smartphone usage. Moreover, the profile of participants with high levels of smartphone usage shows higher scores on the FoMOs and Procrastination scale, and lower scores in the Psychological General Well-Being Index. The results are discussed in light of self-regulation theory.

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Section: Introductionmentioning

confidence: 99%

Problematic Smartphone Use Leads to Behavioral and Cognitive Self-Control Deficits

Fabio

Stracuzzi

Faro

2022

IJERPH

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“…Besides, withdrawal symptoms associated with excessive internet use had the greatest influence in reducing the HRV biofeedback efficiency during short-term training. Similarly, Demin and Poskotinova [80] compared three groups of adolescents between 16 and 17 years old at minimal risk of internet addiction, at high risk of internet addiction, and those with a diagnosis of internet addiction. Apart from HRV, they added EEG measures at baseline and during the short-term HRV biofeedback training period to identify the most sensitive brain regions to HRV biofeedback.…”

Section: Hrv Biofeedbackmentioning

confidence: 99%

Biofeedback Interventions for Impulsivity-related Processes in Addictive Disorders

et al. 2023

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Purpose of Review Biofeedback is a promising technique that has been used as a treatment tool for different psychological disorders. In this regard, central (neurofeedback) and peripheral psychophysiological signals are presented as comprehensible stimuli with the aim of training specific processes. This review summarizes recent evidence about its use for the treatment of impulsivity-related processes in addictive disorders. Recent Findings Neurofeedback (NFB) protocols, based on electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), have focused on substance use disorders. Biofeedback protocols using peripheral measures have been mainly based on heart rate variability and focused on behavioral addictions. EEG-NFB reported good results in the reduction of hyperarousal, impulsivity and risk taking in alcohol use disorder, and decreased rates of smoking and less craving in nicotine addiction. In fMRI-NFB, effective NFB performance has been related with better clinical outcomes in substance use disorders; however, its implication for treatment is still unclear. Heart rate variability biofeedback results are scarce, but some interventions have been recently designed aimed at treating behavioral addictions. Summary In addictive disorders, biofeedback interventions for impulsivity-related processes have shown promising results, although the literature is still scarce. Further research should aim at proving the effectiveness of biofeedback protocols as a treatment option for impulsivity in addictive disorders.

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“… 10 Conversely, biofeedback protocols using peripheral measurements, primarily based on heart rate variability, have centered on behavioral addictions. 11 Therefore, although there have been limited studies applying neurofeedback to internet addiction patients, research utilizing neurofeedback as an additional tool for the treatment of alcohol use disorder has demonstrated effects, influencing attentional bias or lack of inhibition. 12 Studies observing the brainwave characteristics of internet addiction have revealed shared neurophysiological traits with other addictions.…”

Section: Introductionmentioning

confidence: 99%

Impact of Mobile Neurofeedback on Internet Addiction and Neurocognitive Function in Neurotypical Children: Double-Blind, Sham-Controlled Randomized Clinical Trial

Choi,

Yang,

Kim

2024

NDT

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Objective The purpose of this study was to evaluate the positive impact of mobile neurofeedback (MNF) in neurotypical children compared to sham mobile neurofeedback. Methods Neurotypical children aged 10–15 participated in the study. All subjects were assessed using the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version Korean Version (K-SADS-PL-K) and confirmed to have no psychiatric symptoms. The participants were randomly assigned to the MNF active (N=31) or sham control (N=30) groups. The MNF program was administered using a mobile app for 30 min/day, 3 days/week, for 3 months. All participants and their parents completed self-report scales and participants complete neurocognitive function assessments including the continuous performance test, Stroop, children’s color trails test-1 and 2, and intelligence test at baseline and after the 3-month MNF program. Results This study involved 61 participants (mean [SD] age, 11.24 [1.84] years; 30 male participants [49.2%]). To verify the difference between the MNF group and the sham group, 2(MNF-Sham) X 2(Pre-Post) repeated measures ANOVA was performed. The main effect of the K-scale (Korea Internet addiction scale) between-group factor (MNF vs Sham) was not significant, but the main effect of the within-group factor (Pre vs Post) was significant (F=7.595, p=0.008). The interaction effect of between-group factors and within-group factors was also significant (F=5.979, p=0.017). In other self-reported scales of children and parents and neurocognitive function assessments, there was no significant difference between the two groups. Conclusion Active mobile neurofeedback significantly improved children’s K-scale score compared to the sham group. Therefore, mobile neurofeedback could be an easy-to-access therapeutic option for children at risk of Internet addiction. On the other hand, there was no significant difference in other scales and neurocognitive function. A 3-month intervention may not have been long enough to cause change, so longer interventions are needed for confirmation.

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Neurophysiologic Reactions during Heart Rate Variability Biofeedback Session in Adolescents with Different Risk of Internet Addiction

Cited by 5 publications

References 28 publications

Problematic Smartphone Use Leads to Behavioral and Cognitive Self-Control Deficits

Problematic Smartphone Use Leads to Behavioral and Cognitive Self-Control Deficits

Biofeedback Interventions for Impulsivity-related Processes in Addictive Disorders

Impact of Mobile Neurofeedback on Internet Addiction and Neurocognitive Function in Neurotypical Children: Double-Blind, Sham-Controlled Randomized Clinical Trial

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