Abstract
Objective
To examine the effect of mobile neurofeedback training on the clinical symptoms, attention abilities, and execution functions of children with attention deficit hyperactivity disorder (ADHD).
Methods
The participants were 74 children with ADHD aged 8−15 years who visited the Department of Child and Adolescent Psychiatry at Seoul National University Children’s Hospital. The participants were randomly assigned to the mobile neurofeedback (n = 35) or control (sham; n = 39) group. Neurofeedback training was administered using a mobile app (equipped with a headset with a 2-channel electroencephalogram [EEG] sensor) for 30 min/day, 3 days/week, for 3 months. Children with ADHD were individually administered various neuropsychological tests, including the continuous performance test, Children’s Color Trails Test-1 and 2, and Stroop Color and Word Tests. The effects of mobile neurofeedback were evaluated at baseline and at 3 and 6 months after treatment initiation.
Results
Following treatment, both mobile neurofeedback-only and sham-only groups showed significant improvements in attention and response inhibition. In the visual continuous performance test, omission errors decreased to the normal range in the mobile neurofeedback-only group after training, suggesting that mobile neurofeedback effectively reduced inattention in children with ADHD. In the advanced test of attention, auditory response times decreased in the mobile neurofeedback + medication group after training, but increased in the sham+medication group. Overall, there were no significant between-group differences in other performance outcomes.
Conclusion
Mobile neurofeedback may have potential as an additional therapeutic option alongside medication for children with ADHD.
Keywords: Neurofeedback, ADHD, Executive function, Children
INTRODUCTION
Attention deficit hyperactivity disorder (ADHD) accounts for the majority of cases among children/adole-scents who visit the department of child and adolescent psychiatry [1] and almost 50% of cases among all children who visit psychiatric counseling centers [2]. ADHD is a neurodevelopmental disorder characterized by persistent patterns of inattention or hyperactivity and impulsive symptoms that impair daily functioning or growth and development. Compared with normal children, those with ADHD are known to show deficits in working memory, inhibition control, and planning related to the executive function [3]. In the field of neuropsychology, the term ‘executive function’ is used to describe the behaviors of patients with frontal lobe-related problems; as such, it is considered an important component of cognition that consciously controls human behavior and thinking [4]. Children with ADHD find it difficult to suppress and control their responses to various stimuli from the external environment due to defects in executive function. As a result, they experience problems in systematic planning, self-regulation, and self-help.
At present, the treatments for ADHD include medication, behavioral therapy, combined behavioral and medication therapy, cognitive–behavioral therapy, and parental education [5]. Drug treatment has been shown to exert therapeutic effects in children with ADHD by properly maintaining their arousal level, which increases their attention and concentration and reduces impulsivity and hyperactivity [6]. Overall, 70−80% of patients with ADHD who receive drug treatments show improvements in performance [7]. Several studies have also demonstrated the benefits of drug treatment in children with ADHD [8,9]. How-ever, some drugs (such as psychostimulants) are associated with side effects and limitations. Therefore, alternatives to drug treatments are also being investigated. Patients with ADHD sometimes complain of drug-related side effects, such as anorexia or sleep problems [8]. Moreover, continued treatments with central nervous system stimulants can negatively affect children’s health by exerting adverse effects on their cardiovascular system or reducing their growth rate [10]. For these reasons, some parents refuse drug treatment for their child with ADHD, leading to delayed treatment and the re-emergence of ADHD symptoms following discontinuation of the drug [11]. As such, even though drug treatment is recommended as the primary treatment method for ADHD, an alternative mode of treatment is required.
Neurofeedback is a non-invasive technique that has received continuous attention from researchers and has not been reported to have significant side effects [3]. It is expected to help children with ADHD who find it difficult to continue drug treatment because of the associated side effects [12]. Neurofeedback training allows users to learn how to self-regulate their body, while providing real-time feedback based on their brain wave patterns [13]. Neuro-feedback has been incorporated into the treatment of ADHD, and several clinical studies have reported its effectiveness in children with ADHD. Neurofeedback treatment has been shown to improve concentration, reduce impulsivity, and control hyperactivity in children with ADHD by altering their brain waves [14,15]. Rossiter and La Vaque [16] studied patients with ADHD and reported that neurofeedback treatment showed the same effectiveness as drug treatment. Linden et al. [17] conducted a randomized controlled study and reported significant improvements in intelligence in the ADHD group following neurofeedback treatment. Moreover, Monastra et al. [18] noted improvements in behavioral problems and attention in patients with ADHD who received neurofeedback (compared with patients who underwent drug treatment).
Neurofeedback treatment for ADHD patients has also been investigated in the Korean population [19]. Yoo [20] verified the effectiveness of neurofeedback training by conducting 30 sessions of neurofeedback in children with ADHD. In the group that received neurofeedback training combined with drug treatment, the results of the continuous performance test (CPT) showed a significant reduction in the number of omission and commission errors, average response time, and standard deviation of the response time. However, few studies have evaluated the effectiveness of neurofeedback training in Korean children with ADHD, and existing studies have mainly been conducted by visiting hospitals and under the guidance of experts. These temporal and spatial limitations limit the frequency of such studies, and their results are difficult to apply in clinical settings.
Digital-based care has the potential to improve therapeutic interventions in terms of cost-effectiveness, safety, and accessibility [21]. Therefore, in this study, we aimed to verify the effectiveness of mobile neurofeedback (MNF) training using an easily accessible MNF system in children and adolescents who are accustomed to using smart-phones. We performed a randomized clinical trial and conducted observations and evaluations at 3 and 6 months after the initiation of MNF training. The effectiveness of MNF training was verified based on the participants’ scores on the Clinical Global Impression-severity scale (CGI-S), Children-Global Assessment Scale (C-CAS), and Korean ADHD Rating Scale-IV (hereinafter referred to as ARS). The results of the computerized Advanced Test of Attention (ATA) and two executive function tests (the Children’s Color Trails Test, CCTT; and the Stroop Color- Word Test) were used to examine the clinical symptoms of ADHD in participants and to evaluate changes in their attention and executive functions.
METHODS
Participants
The participants of this study were 74 children and adolescents with ADHD and their parents who visited the outpatient Department of Child and Adolescent Psychiatry at Seoul National University Hospital from January 2020 to December 2021. The participants were diagnosed by a child and adolescent psychiatrist and clinical psychologist according to the ADHD diagnosis criteria in the Diag-nostic and Statistical Manual of Mental Disorders 5th edition (DSM-5). Participants who satisfied all inclusion and exclusion criteria and agreed to participate in the study were included. The study design was explained to the parents and children, and written informed consent was obtained from each participant. This study was approved by the Institutional Review Board of Seoul National University Hospital (IRB approval number: H-1905-145-1035).
The inclusion criteria were as follows:
i. Patients aged 8−15 years.
ii. Patients diagnosed with ADHD according to the DSM-5 diagnostic criteria and Kiddie–Schedule for Affective Disorders and Schizophrenia-Present and Lifetime Version (K-SADS-PL).
iii. Patients with verbal comprehension index score of ≥ 80 and a full scale intelligence quotient (FSIQ) score of ≥ 70 measured by Korean–Wechsler Intelligence Scale for Children-Fourth Edition (K-WISC-IV).
iv. Neurofeedback or sham-only treatment groups: patients who had not received methylphenidate/atomoxetine drug treatment in the past or those who had received methylphenidate drug treatment in the past (for a period of < 1 year) but had not taken methylphenidate/atomoxetine within 4 weeks of participating in the study.
v. Neurofeedback or sham adjunctive treatment groups: ADHD patients who meet criteria i, ii, and iii; were undergoing drug treatment with methylphenidate or atomoxetine; had not achieved remission; scored ≥ 4 points on the CGI-S scale; and had not changed their drug dosage in the preceding month.
The exclusion criteria were as follows:
i. Patients diagnosed with any congenital genetic disease.
ii. Patients with a clear history of acquired brain damage (such as cerebral palsy).
iii. Patients with convulsive disorders, other neurological diseases, or uncorrected sensory disorders.
iv. Patients with a history of schizophrenia or other childhood psychosis.
v. Patients with a verbal comprehension index score of < 80 or a FSIQ score of < 70 measured by K-WISC- IV.
vi. Patients with obsessive-compulsive disorder, major depressive disorder, or bipolar disorder.
Study Design
At the first visit, a child and adolescent psychiatrist conducted a clinical evaluation using the K-SADS-PL, CGI, and C-GAS to determine whether the patient met the inclusion criteria of the study. Following this, various neuropsychological tests (intelligence test, Stroop, CCTT, and ATA) and a parent questionnaire (ADHD rating scale) were administered. Participants who completed the baseline tests were divided into two groups: the MNF training group (received only MNF training) and the MNF + medication group (received MNF training combined with drug treatment, depending on whether the existing drug treatment was provided). In addition, participants in the MNF and MNF + medication groups were randomly assigned to the neurofeedback training and sham control groups, respectively. The researcher, clinical/neuropsychological evaluator, participants, and the guardians of participants were blinded to the neurofeedback/sham control grouping status of participants until the end of the study. In the monotherapy and drug treatment groups, the assigned treatment drug was maintained for 12 months in order to confirm the neurofeedback effect. The effects of MNF training were compared and analyzed at 3 months (imme-diately after the end of training) and at the 6-month follow-up (3 months after the end of training).
Mobile Neurofeedback
A study was conducted to adjust the theta/beta ratio using OmniCNS, which is a portable app-based neurofeedback training device. The OMNIFIT BRAIN: The Focus-an application equipped with an audio guide and four types of games-was used for neurofeedback training. Following pre-evaluation, the researcher provided guidance to the participants and their guardians on how to use the application and demonstrated its usage. The participants were instructed to play the game for 30 min/day, 3 days/week, for 3 months. To facilitate attention and cognitive training via neurofeedback, the games were played using a headset equipped with 2-channel electroencephalogram (EEG) sensors for the left and right frontal lobes. The sham control group used the same device and program, but received random feedback regardless of the actual EEG readings.
Outcome Measures
The results of CGI-S and C-GAS assessments, parents’ responses to self-administered questionnaires, and scores of neuropsychological tests (such as tests to assess attention and frontal lobe function) were used to evaluate MNF-induced changes in symptoms among children with ADHD.
Clinical Global Impression-Severity (CGI-S)
The CGI-S scale [22] is widely used for the objective evaluation of treatment effects in clinical drug research. This tool was developed to assess the severity of a patient’s clinical symptoms at the time of assessment based on the clinician’s past experiences with patients with the same diagnosis. It is evaluated on a scale ranging from 1 point (not ill) to 7 points (extremely severe symptoms); the higher the score, the higher the severity of disease.
Children-Global Assessment Scale (C-CAS)
The C-GAS, developed by Shaffer et al. [23], is a tool for measuring overall disability and requires clinicians to record the functional level of children and adolescents as a numerical value on a scale of 1−100. A higher score indicates better function. Evaluators are expected to consider the child’s behavioral and emotional functioning when at home with family, at school with peers, and during leisure time.
Korean ADHD Rating Scale-IV (K-ARS)
The ARS was developed by DuPaul [24] as a tool to evaluate ADHD symptoms in school-age children. Subse-quently, a Korean version of the ARS was developed and standardized [25]. The ARS consists of 18 questions related to the diagnostic criteria of ADHD in the DSM-IV. Each item is rated on a 4-point scale ranging from 0 to 3 points depending on the frequency of the child’s behavioral problems; a score of ≥ 2 is considered abnormal relative to the child’s developmental stage. The total score of the odd-numbered items measures attention-deficit symptoms, whereas that of the even-numbered items measures hyperactive-impulsive symptoms.
Advanced Test of Attention (ATA)
The ATA is a computerized CPT that is used to evaluate attention and can measure concentration ability and impulse control. The Korean version of the ATA was developed and standardized by Cho et al. [26]. The ATA measures four indices: the number of omission errors (reflect-ing the symptoms of inattention); number of commission errors (indicative of cognitive and behavioral impulsivity); mean response time (reflecting the speed at which target stimuli are processed); and the standard deviation of response time (indicating the consistency of attentional control).
Children’s Color Trails Test (CCTT)
The Korean version of the color line test for children is the Trail Making Test, and it was modified and supplemented by Koo and Shin [27] to minimize the influence of culture and language. To minimize the influence of language and age on the CCTT, the modified version uses colors and numbers instead of letters. The CCTT-1 requires participants to quickly connect the numbers from 1 to 15, whereas the CCTT-2 requires participants to sequentially connect the numbers from 1 to 15 using alternating colors. The interference index is calculated based on the results of this test. Both CCTT-1 and CCTT-2 measure visual sequential processing ability and psychomotor speed, and CCTT-2 measures divided attention as well.
Stroop Color and Word Test (Stroop test)
The Stroop test used in this study was a standardized Korean version of the Stroop Color-Word Test for children developed by Shin and Park [28]. This is a tool for neuropsychological evaluation that evaluates the efficiency of the inhibitory process in the frontal lobe. The first task (the word task) requires participants to read the presented letters as quickly as possible, whereas the second task (the color task) assesses their ability to name colors as quickly as possible. The final color-word task requires participants to name a color while suppressing the automated response to the presented letters (under the condition that the color of the word does not match the letters). The interference score is calculated as the difference in scores between the color and color-word tasks.
Statistical Analysis
In this study, we evaluated the effect of MNF training on improving clinical symptoms and executive function in children and adolescents with ADHD. The participants were administered the CGI-S, C-GAS, ARS, CPT (ATA), Stroop test, and CCTT before MNF training (baseline) and at 3 and 6 months after treatment initiation. We performed a 2 (MNF vs. sham) × 3 (baseline vs. 3 months vs. 6 months) mixed-model repeated measures analysis of variance (ANOVA) to verify the differences in effects between the MNF and the sham control group. Statistical analyses were performed in SPSS 22.0 (IBM Co.) with a signifi-cance level of p < 0.05.
RESULTS
Demographic Characteristics of Participants
Of the 81 recruited participants, 7 did not meet the inclusion criteria or withdrew their consent to participate in the study before randomization. Therefore, the effect of MNF training was verified using data from 74 participants who were tested at baseline. The MNF and MNF + medication groups were divided into a neurofeedback training group and a sham control group, respectively. The results at baseline and at 3 and 6 months after treatment initiation were analyzed and compared (Fig. 1).
Fig. 1.
Flowchart of the participant selection process in each phase of the study.
MNF, mobile neurofeedback.
Table 1 summarizes the demographic, clinical, and neuropsychological characteristics of participants. At base-line, there were no statistically significant differences in demographic characteristics, clinical characteristics, or neuropsychological scores between the neurofeedback and sham control groups (p > 0.05). However, there were differences in ATA auditory omission errors between the MNF-only and sham-only groups (p = 0.012) and in ARS inattention scores between the MNF + medication and sham + medication groups (p = 0.045).
Table 1.
Demographics and baseline clinical characteristics of participants
| Variable | Total (n = 74) | MNF-only (n = 8) | Sham-only (n = 10) | MNF + medication (n = 27) | Sham + medication (n = 29) |
|---|---|---|---|---|---|
| Sex (M/F) | 61/13 | 7/1 | 8/2 | 23/4 | 24/6 |
| Age (yr) | 9.92 ± 1.93 | 8.50 ± 1.07 | 9.30 ± 1.83 | 9.97 ± 1.88 | 10.52 ± 2.01 |
| FSIQ | 98.50 ± 15.52 | 109.63 ± 12.48 | 105.20 ± 13.89 | 96.66 ± 14.62 | 94.70 ± 16.24 |
| CGI-S | 4.12 ± 0.33 | 4.13 ± 0.35 | 4.30 ± 0.48 | 4.10 ± 0.31 | 4.07 ± 0.27 |
| C-GAS | 63.34 ± 8.91 | 66.25 ± 9.54 | 58.60 ± 8.41 | 64.21 ± 8.94 | 63.30 ± 8.69 |
| ARS-inattention | 13.04 ± 6.10 | 12.13 ± 5.28 | 14.60 ± 7.07 | 14.50 ± 5.59* | 11.22 ± 6.23 |
| ARS-hyperactivity/impulsivity | 9.48 ± 6.24 | 10.75 ± 4.43 | 12.50 ± 6.98 | 9.68 ± 6.21 | 7.78 ± 6.22 |
| ARS-total | 21.86 ± 11.19 | 21.88 ± 9.54 | 27.10 ± 13.84 | 22.61 ± 9.71 | 18.85 ± 11.72 |
| CCTT-1 | 52.71 ± 11.99 | 51.13 ± 15.10 | 55.30 ± 6.91 | 50.90 ± 12.77 | 54.18 ± 11.86 |
| CCTT-2 | 52.59 ± 9.63 | 56.13 ± 10.11 | 57.00 ± 4.97 | 50.52 ± 10.15 | 52.15 ± 9.86 |
| Stroop word | 44.82 ± 11.94 | 51.38 ± 16.70 | 49.70 ± 9.13 | 41.86 ± 10.37 | 44.26 ± 12.17 |
| Stroop color | 47.41 ± 10.69 | 50.75 ± 7.85 | 47.40 ± 7.75 | 44.52 ± 12.44 | 49.52 ± 9.98 |
| Stroop color-word | 45.66 ± 12.24 | 48.00 ± 11.89 | 47.10 ± 10.58 | 45.79 ± 11.92 | 44.30 ± 13.66 |
| ATA visual omission | 63.41 ± 18.39 | 65.00 ± 20.16 | 67.00 ± 20.14 | 64.24 ± 17.97 | 60.78 ± 18.47 |
| ATA visual commission | 63.10 ± 19.60 | 56.00 ± 22.57 | 70.60 ± 22.31 | 62.62 ± 18.50 | 62.67 ± 19.23 |
| ATA visual RT | 68.70 ± 15.73 | 76.14 ± 14.65 | 70.10 ± 26.32 | 67.28 ± 13.84 | 67.78 ± 18.15 |
| ATA visual RT SD | 63.53 ± 21.06 | 63.29 ± 17.69 | 72.10 ± 18.00 | 64.03 ± 22.09 | 59.89 ± 21.81 |
| ATA auditory omission | 67.52 ± 21.73 | 55.14 ± 11.44* | 77.20 ± 17.94 | 61.69 ± 19.26 | 73.41 ± 24.80 |
| ATA auditory commission | 70.99 ± 20.89 | 61.86 ± 19.96 | 79.20 ± 17.63 | 66.00 ± 19.41 | 75.67 ± 22.44 |
| ATA auditory RT | 57.37 ± 13.40 | 59.43 ± 18.24 | 58.80 ± 4.80 | 57.41 ± 10.36 | 56.26 ± 17.14 |
| ATA auditory RT SD | 50.34 ± 13.71 | 49.86 ± 13.68 | 56.40 ± 11.06 | 48.30 ± 13.98 | 50.59 ± 14.32 |
Values are presented as number only or mean ± standard deviation.
MNF, mobile neurofeedback; FSIQ, full-scale intelligence quotient; CGI-S, Clinical Global Impression-Severity scale; C-CAS, Children-Global Assessment Scale; ARS, Korean ADHD Rating Scale-IV; CCTT, Children’s Color Trails Test; ATA, Advanced Test of Attention; RT, response time; RT SD, standard deviation of the response time.
*p < 0.05.
Comparison of Results at Baseline, 3 Months, and 6 Months between the MNF-only and Sham-only Groups
To evaluate the differences in performance outcomes between the MNF-only and sham-only groups, we performed a 2 (MNF vs. sham) × 3 (baseline vs. 3 months vs. 6 months) mixed-design ANOVA (Table 2). There were no significant between-group differences in outcomes, except in C-GAS scores (F = 8.98, p < 0.05). Over time, both groups showed significant improvements in the scores for C-GAS (F = 7.07, p < 0.01), ARS-inattention (F = 3.74, p < 0.05), ARS-total (F = 3.71, p < 0.05), Stroop words task (F = 5.21, p < 0.05), and Stroop color task (F = 5.19, p < 0.05), as well as in commission errors (F = 4.78, p < 0.05) in the auditory ATA tests. An interaction effect was found for omission errors in the visual task during the ATA (F = 6.07, p < 0.05). In the MNF-only group, omission errors decreased after 3 months of treatment, and this trend was maintained until the 6-month follow-up. In contrast, although the sham control group showed an improvement in omission errors at 3 months, the errors tended to increase rapidly at 6 months after treatment (Fig. 2).
Table 2.
Differences in performance between the mobile neurofeedback (MNF)-only and sham-only treatment groups at baseline and at 3 and 6 months after treatment initiation (n = 11)
| Variable | MNF-only (n = 4) | Sham-only (n = 7) | F (group) | F (time) | F (group × time) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||||
| Baseline | 3 months | 6 months | Baseline | 3 months | 6 months | |||||
| CGI-S | 4.13 ± 0.35 | 3.50 ± 1.07 | 3.63 ± 1.19 | 4.29 ± 0.49 | 4.00 ± 0.58 | 4.00 ± 0.58 | 1.11 | 2.77 | 0.34 | |
| C-GAS | 66.25 ± 9.54 | 73.88 ± 9.26 | 73.88 ± 9.50 | 55.86 ± 7.20 | 61.71 ± 8.28 | 61.71 ± 8.28 | 8.98* | 7.07** | 0.12 | |
| ARS | ||||||||||
| ARS-inattention | 12.00 ± 5.69 | 8.86 ± 4.67 | 10.00 ± 4.40 | 15.75 ± 5.66 | 13.25 ± 4.99 | 13.00 ± 4.69 | 1.69 | 3.74* | 0.20 | |
| ARS-hyperactivity/impulsivity | 10.71 ± 4.79 | 8.00 ± 5.16 | 8.00 ± 4.28 | 13.50 ± 6.56 | 11.50 ± 6.03 | 10.75 ± 5.85 | 1.03 | 2.76 | 0.06 | |
| ARS-total | 22.71 ± 10.29 | 16.86 ± 9.62 | 18.00 ± 8.29 | 29.25 ± 11.53 | 24.75 ± 10.87 | 23.75 ± 10.53 | 1.38 | 3.71* | 0.12 | |
| CCTT | ||||||||||
| CCTT-1 | 54.86 ± 11.67 | 58.29 ± 8.04 | 59.43 ± 7.04 | 54.00 ± 5.10 | 52.75 ± 7.68 | 59.43 ± 7.04 | 1.12 | 2.23 | 1.07 | |
| CCTT-2 | 58.29 ± 8.64 | 57.86 ± 7.15 | 60.14 ± 6.34 | 57.25 ± 6.70 | 58.00 ± 5.89 | 57.00 ± 11.51 | 0.06 | 0.04 | 0.26 | |
| Stroop | ||||||||||
| Word | 47.57 ± 19.98 | 52.71 ± 13.88 | 52.71 ± 13.88 | 48.25 ± 6.18 | 50.50 ± 7.05 | 56.75 ± 9.67 | 0.44 | 5.21* | 0.04 | |
| Color | 50.00 ± 9.31 | 51.71 ± 9.55 | 55.86 ± 13.20 | 49.50 ± 10.25 | 54.25 ± 9.00 | 60.50 ± 11.70 | 0.03 | 5.19* | 1.05 | |
| Color-word | 46.00 ± 14.06 | 52.43 ± 11.52 | 61.43 ± 16.70 | 47.25 ± 12.82 | 52.25 ± 14.01 | 59.50 ± 10.97 | 0.02 | 3.33 | 0.03 | |
| Visual ATA | ||||||||||
| Omission | 61.00 ± 18.21 | 57.14 ± 19.17 | 50.57 ± 3.64 | 60.50 ± 23.23 | 50.75 ± 10.97 | 81.25 ± 20.22 | 0.56 | 1.81 | 6.07* | |
| Commission | 61.29 ± 21.69 | 56.14 ± 19.24 | 46.29 ± 7.50 | 73.00 ± 21.32 | 53.50 ± 11.45 | 55.00 ± 11.58 | 1.06 | 2.75 | 0.46 | |
| RT | 70.86 ± 10.76 | 72.57 ± 17.15 | 70.14 ± 14.68 | 64.25 ± 14.59 | 69.25 ± 7.23 | 77.50 ± 13.53 | 0.12 | 1.57 | 2.95 | |
| RT SD | 62.71 ± 17.69 | 58.86 ± 15.14 | 57.14 ± 11.16 | 67.50 ± 23.33 | 59.00 ± 22.80 | 77.75 ± 21.48 | 0.51 | 0.88 | 1.48 | |
| Auditory ATA | ||||||||||
| Omission | 55.43 ± 11.72 | 51.57 ± 10.98 | 48.71 ± 8.94 | 75.75 ± 21.93 | 68.00 ± 23.85 | 73.75 ± 30.34 | 3.81 | 1.15 | 0.48 | |
| Commission | 62.86 ± 19.50 | 51.86 ± 14.00 | 47.57 ± 13.04 | 81.00 ± 21.68 | 61.75 ± 21.93 | 73.50 ± 26.71 | 2.30 | 4.78* | 1.30 | |
| RT | 55.86 ± 15.56 | 57.86 ± 8.30 | 54.71 ± 8.90 | 57.25 ± 6.50 | 60.75 ± 3.77 | 56.75 ± 5.32 | 0.13 | 0.52 | 0.00 | |
| RT SD | 47.57 ± 12.82 | 40.86 ± 10.70 | 38.14 ± 4.98 | 55.25 ± 13.30 | 44.00 ± 9.56 | 58.25 ± 26.73 | 2.19 | 1.80 | 1.42 | |
Values are presented as mean ± standard deviation.
CGI-S, Clinical Global Impression-Severity scale; C-CAS, Children-Global Assessment Scale; ARS, Korean ADHD Rating Scale-IV; CCTT, Children’s Color Trails Test; ATA, Advanced Test of Attention; RT, response time; RT SD, standard deviation of the response time.
*p < 0.05, **p < 0.01.
Fig. 2.
Changes in mean visual omission errors (advanced test of attention) in the mobile neurofeedback (MNF)-only and sham-only treatment groups at baseline and at 3 and 6 months after treatment initiation.
The differences in performance outcomes by task were further examined in detail. The MNF-only group showed improved performance in CCTT-1 (reflecting visual tracking ability) and CCTT-2 (reflecting divided attention), although the differences were not statistically significant. The sham-only group showed improved performance only in CCTT-1. In the ATA visual task, the omission and commission errors, response time, and standard deviation of the response time decreased in the MNF-only group, although the differences were not statistically significant. In the sham-only group, commission errors and response time fluctuations decreased at 3 months and then increased at 6 months after treatment. The scores of all other tasks also increased in the sham-only group, indicating that MNF training had no significant effect on improving attentional performance. In the ATA auditory task, both groups showed a significant decrease in commission errors at 3 months after treatment (F = 4.78, p < 0.05). At 6 months after treatment, commission errors decreased further in the MNF-only group and increased in the sham- only group. The standard deviation of the response time showed a similar pattern. That is, the values decreased in both groups at 3 months; at 6 months after treatment, the values decreased further in the MNF-only group and increased in the sham-only group.
Comparison of Results at Baseline, 3 Months, and 6 Months between the MNF + Medication and sham + Medication Groups
To evaluate the differences in performance outcomes between the MNF + medication and sham + medication groups, we performed a 2 (MNF + medication vs. sham + medication) × 3 (baseline vs. 3 months vs. 6 months) mixed-design repeated measures ANOVA (Table 3). There were significant between-group differences in the scores for ARS-inattention (F = 4.17, p < 0.05) and Stroop color test (F = 6.14, p < 0.05). Over time, both groups showed significant improvements in the scores for CGI-S (F = 19.06, p < 0.001), C-GAS (F = 18.96, p < 0.001), ARS- hyperactivity/impulsivity (F = 6.11, p < 0.01), ARS-total (F = 3.61, p < 0.05), Stroop word task (F = 14.48, p < 0.001), Stroop color task (F = 11.64, p < 0.001), and Stroop color-word task (F = 7.03, p < 0.01), as well as in commission errors in the visual (F = 7.76, p < 0.01) and auditory (F = 8.40, p < 0.01) ATAs. However, there were no significant differences in outcomes between groups. We found an interaction effect on the response time to the ATA auditory task. In the MNF + medication group, response times were shorter at 3 and 6 months after treatment than at baseline, indicating a significant improvement in the participants’ ability to respond promptly to target stimuli. However, in the sham + medication group, the response times were longer at 3 and 6 months after treatment than at baseline (F = 4.72, p < 0.05) (Table 3, Fig. 3).
Table 3.
Differences in performance between the mobile neurofeedback (MNF) + medication and sham + medication groups at baseline and at 3 and 6 months after treatment initiation (n = 40)
| Variable | MNF + medication (n = 19) | Sham + medication (n = 21) | F (group) | F (time) | F (group × time) | |||||
|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||||
| Baseline | 3 months | 6 months | Baseline | 3 months | 6 months | |||||
| CGI-S | 4.11 ± 0.32 | 3.68 ± 0.58 | 3.68 ± 0.58 | 4.10 ± 0.30 | 3.67 ± 0.58 | 3.67 ± 0.58 | 0.00 | 19.06*** | 0.06 | |
| C-GAS | 61.37 ± 7.53 | 66.47 ± 8.94 | 67.95 ± 9.89 | 65.81 ± 7.45 | 70.05 ± 8.07 | 70.48 ± 8.20 | 2.15 | 18.96*** | 0.48 | |
| ARS | ||||||||||
| ARS-inattention | 13.32 ± 5.52 | 11.91 ± 5.07 | 11.91 ± 5.61 | 10.39 ± 6.70 | 8.06 ± 5.55 | 8.94 ± 6.62 | 4.17* | 2.83 | 0.20 | |
| ARS-hyperactivity/impulsivity | 8.36 ± 5.92 | 6.86 ± 5.62 | 7.05 ± 6.72 | 7.72 ± 6.51 | 4.89 ± 4.71 | 5.83 ± 5.31 | 0.56 | 6.11** | 0.54 | |
| ARS-total | 20.72 ± 9.51 | 18.77 ± 9.82 | 18.95 ± 11.49 | 18.11 ± 12.44 | 12.94 ± 8.88 | 14.78 ± 10.50 | 2.05 | 3.61* | 0.69 | |
| CCTT | ||||||||||
| CCTT-1 | 53.28 ± 8.56 | 56.22 ± 8.00 | 55.28 ± 8.12 | 57.43 ± 14.02 | 56.14 ± 11.25 | 56.00 ± 8.40 | 0.33 | 0.11 | 0.76 | |
| CCTT-2 | 52.33 ± 7.47 | 56.00 ± 6.68 | 54.39 ± 7.11 | 52.71 ± 9.88 | 54.86 ± 10.29 | 55.29 ± 9.47 | 0.00 | 1.96 | 0.23 | |
| Stroop | ||||||||||
| Word | 42.89 ± 8.90 | 47.78 ± 8.42 | 51.28 ± 11.59 | 45.36 ± 12.06 | 53.00 ± 8.79 | 56.57 ± 10.04 | 2.28 | 14.48*** | 0.38 | |
| Color | 45.06 ± 10.03 | 48.72 ± 10.10 | 48.22 ± 8.30 | 50.86 ± 11.17 | 55.86 ± 9.16 | 58.21 ± 7.31 | 6.14* | 11.64*** | 1.69 | |
| Color-word | 46.28 ± 10.40 | 51.11 ± 12.17 | 51.11 ± 12.38 | 43.29 ± 14.86 | 55.79 ± 12.39 | 55.50 ± 10.99 | 0.43 | 7.03** | 1.35 | |
| Visual ATA | ||||||||||
| Omission | 59.33 ± 15.89 | 61.11 ± 19.58 | 59.39 ± 20.44 | 55.64 ± 12.22 | 55.14 ± 21.95 | 55.57 ± 19.93 | 0.62 | 0.03 | 0.10 | |
| Commission | 59.61 ± 16.49 | 61.89 ± 15.37 | 51.83 ± 13.25 | 59.14 ± 19.52 | 56.50 ± 19.49 | 51.64 ± 15.83 | 0.15 | 7.76** | 0.00 | |
| RT | 67.67 ± 14.89 | 63.33 ± 16.65 | 66.50 ± 16.83 | 66.00 ± 18.81 | 66.14 ± 16.66 | 70.71 ± 18.35 | 0.11 | 1.31 | 0.83 | |
| RT SD | 63.06 ± 22.72 | 57.67 ± 16.50 | 54.50 ± 20.19 | 52.57 ± 17.25 | 52.86 ± 23.98 | 56.43 ± 22.40 | 0.46 | 0.49 | 2.36 | |
| Auditory ATA | ||||||||||
| Omission | 56.78 ± 15.07 | 55.94 ± 17.50 | 55.72 ± 21.27 | 68.64 ± 25.90 | 66.64 ± 25.16 | 67.29 ± 26.27 | 2.54 | 0.17 | 0.03 | |
| Commission | 59.94 ± 17.12 | 55.94 ± 14.02 | 49.06 ± 13.39 | 70.71 ± 21.42 | 65.07 ± 21.80 | 63.86 ± 19.66 | 3.93 | 8.40** | 0.91 | |
| RT | 60.61 ± 10.73 | 57.78 ± 10.85 | 57.28 ± 9.58 | 57.36 ± 13.20 | 63.43 ± 16.61 | 62.43 ± 15.90 | 0.37 | 0.50 | 4.72* | |
| RT SD | 46.00 ± 11.93 | 42.83 ± 11.01 | 41.39 ± 11.88 | 49.71 ± 16.39 | 46.00 ± 13.02 | 46.64 ± 15.63 | 0.89 | 3.03 | 0.20 | |
Values are presented as mean ± standard deviation.
CGI-S, Clinical Global Impression-Severity scale; C-CAS, Children-Global Assessment Scale; ARS, Korean ADHD Rating Scale-IV; CCTT, Children’s Color Trails Test; ATA, Advanced Test of Attention; RT, response time; RT SD, standard deviation of the response time.
*p < 0.05, **p < 0.01, ***p < 0.001.
Fig. 3.
Changes in mean auditory response times (advanced test of attention) in the mobile neurofeedback (MNF) + medication and sham + medication treatment groups at baseline and at 3 and 6 months after treatment initiation.
The MNF + medication group showed improvements in performance in the CCTT, Stroop, and ATA tasks, although the differences were not statistically significant. Both groups showed improved performance in CCTT-2. However, in CCTT-1, performance decreased in the sham + medication group. Both groups showed improvements in the Stroop word, color, and color-word tasks, which reflect the participants’ ability to rapidly process simple word and color stimuli and response inhibition, respectively.
In the computerized ATA, the commission errors, response time, and standard deviation of the response time decreased in the MNF + medication group, although omission errors increased slightly in the visual ATA task. In the sham + medication group, the number of commission errors decreased slightly, the number of omission errors showed no significant change, and the response time and its standard deviation increased. In the auditory ATA task, the commission errors, response time, and standard deviation of the response time decreased in the MNF + medication group, although omission errors showed no significant change.
DISCUSSION
MNF training systems for children with ADHD are currently being developed [29]. However, neurofeedback training remains a new concept in Korea, and there is insufficient research verifying its effectiveness. Therefore, in this study, we investigated whether the clinical symptoms of ADHD, executive function, and attention and concentration abilities of children with ADHD could be improved with MNF training.
We found that after treatment, all groups (the MNF-only, sham-only, MNF + medication, and sham + medication groups) showed a tendency toward improved performance in most tests evaluating the participants’ clinical symptoms and neurocognitive function. Although there were no significant between-group differences, we found an interaction effect in omission errors in the ATA visual task before and after treatment between the MNF-only and sham-only treatment groups. That is, both groups showed improvements immediately after training; however, the treatment effect was maintained until 6 months post-treatment only in the MNF group. These results suggest that MNF treatment may be effective in reducing inattention-related symptoms in visual attention tests. Another interaction effect was found in auditory response times in the computerized ATA between the MNF + medication and sham + medication groups before and after treatment. The MNF + medication group showed improvements in auditory response time, whereas the sham + medication group showed slower response times after the treatment. Since both groups received the drug treatment, this result suggests that MNF training-in addition to drug treatment- effectively improved the participants’ dexterity of responses to auditory stimuli.
Our results suggest that MNF training results in improved attention in children with ADHD, which is consistent with the findings of previous studies that have verified the effectiveness of neurofeedback training. A previous study by Heinrich et al. [29] reported that the total ARS score and the commission errors in CPT improved after neurofeedback training. Roh et al. [1] showed that neurofeedback improves attention in children with ADHD. A study on children with ADHD reported improvements in commission errors and response time on the CPT after neurofeedback training [30]. Taken together, these results suggest that neurofeedback effectively improves attention abilities in children with ADHD and may be used as a therapeutic method to treat inattention and sluggishness in these patients. In this study, most of neurocognitive functions and clinical symptoms tended to improve after treatment in the sham control group as well. This may be because of the placebo effect [31]; that is, the expectation of improved performance after receiving a new digital therapeutic intervention may partially explain the improvements in both groups. Additionally, some common mechanisms-such as focused attention and training during the game itself-may have led to improvements in both groups [32].
The strength of this study is that it investigated the effect of MNF training on executive functions of the frontal lobe (which can be considered the core of ADHD symptoms) and attention concentration in children with ADHD. We used a multilateral approach to evaluate the effectiveness of MNF training, including clinician evaluation, guardian evaluation, and objective neuropsychological tests. Our results suggest that MNF can be used as an alternative or auxiliary treatment method for children and adolescents with ADHD who have difficulty continuing drug treatment because of its side effects. In addition, we expect that MNF will contribute to future research and treatment strategies for children with ADHD.
This study also has some limitations. First, the MNF program used in this study only implemented theta/beta neurofeedback, which is a standard ADHD protocol. There-fore, these results cannot be generalized to neurofeedback based on other standard protocols such as slow cortical potentials, sensory motor rhythms, or fMRI. Second, the sample size was relatively small, and there were more participants in the MNF + medication group than in the MNF-only group. Using medication, which has a significant therapeutic effect, makes it difficult to compare the effect of MNF and sham intervention directly. There-fore, the results may not be generalizable. Future studies should include a larger number of participants in the drug-naïve ADHD group in order to verify the effectiveness of MNF training. Third, several participants with ADHD were within the normal range upon neuropsychological examination. Therefore, future research should focus on children with ADHD who exhibit more severe symptoms (outside the normal range). Fourth, children with ADHD can be divided into three subtypes: attention deficit predominance, hyperactivity-impulsivity predominance, and mixed-type. The symptoms, treatment progress, and treatment effects may vary depending on the type of treatment administered. In addition, several diseases may also coexist in children with ADHD. However, this study lacked controls for these factors, and future studies with more control groups are needed.
Conclusion
In this study, children with ADHD were administered MNF training, and their clinical symptoms were evaluated by clinicians and guardians. The attention test, CCTT, and Stroop tasks were administered, and the scores were compared between baseline and at the 3- and 6-month follow-ups. Our results confirmed that MNF training was associated with improved scores in some tasks and that the effect was maintained for 6 months after treatment. Therefore, our findings suggest that MNF training can be used as an auxiliary treatment method for children with ADHD who do not show sufficient improvements with medication treatment. Although some studies have reported no clear improvement in ADHD symptoms following MNF training, domestic studies have reported mixed results. Therefore, continuous follow-up studies are warranted to supplement the limitations of this study.
Funding Statement
Funding This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHDI) funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HI19C0844), by the Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government (MSIT) (No. 2022- 0-00375-001, The Development of Biomarkers based on Individualized Composite Digital Therapeutics for the relief of Aberrant Behaviors in Autism Spectrum Disorder, 20%), and by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-2020M3E5D9080787).
Footnotes
Conflicts of Interest
No potential conflict of interest relevant to this article was reported.
Author Contributions
Organized the project: Min-Sup Shin, Bung-Nyun Kim. Contributed to collecting the data and literature review: Hanbyul Shin, Wooseok Choi. Contributed to data management and data analysis: Seo Young Kwon, You Bin Lim. Contributed to the study conceptualization and analysis plan: Seo Young Kwon, Min-Sup Shin, Bung-Nyun Kim. Contributed useful comments and to writing the manuscript: Gyujin Seo, Mirae Jang. Contributed to drafting of the manuscript: Seo Young Kwon, Gyujin Seo. All authors critiqued the work for intellectual content and approved it for submission.
References
- 1.Roh OB, Son CN, Park TW, Park S. The effects of neurofeedback training on inattention and hyperactivity/impulsivity in children with ADHD. Korean J Clin Psychol. 2011;30:397–418. doi: 10.15842/kjcp.2011.30.2.003. [DOI] [Google Scholar]
- 2.Cantwell DP, Baker L. Stability and natural history of DSM-III childhood diagnoses. J Am Acad Child Adolesc Psychiatry. 1989;28:691–700. doi: 10.1097/00004583-198909000-00009. [DOI] [PubMed] [Google Scholar]
- 3.Ji Y, Won GH, Kim JW. Neurofeedback: Possibility of alternative non-pharmaceutical treatment for children with ADHD. J Korean Soc Biol Ther Psychiatry. 2020;26:195–202. [Google Scholar]
- 4.Zelazo PD, Müller U. The balance beam in the balance: reflections on rules, relational complexity, and developmental processes. J Exp Child Psychol. 2002;81:458–465. doi: 10.1006/jecp.2002.2667. [DOI] [PubMed] [Google Scholar]
- 5.Corsini RJ. Handbook of innovative therapy. John Wiley & Sons Inc; 2001. [Google Scholar]
- 6.Park NS, Oh KJ. Effects of methylphenidate treatment on cognitive behavioral symptoms and social, academic & emotional adjustment of ADHD children. Korean J Clin Psychol. 1992;11:235–248. [Google Scholar]
- 7.Barkley RA, Poillion MJ. Attention deficit hyperactivity disorder: a handbook for diagnosis and treatment. Behav Disord. 1994;19:150–152. doi: 10.1177/019874299401900205. [DOI] [Google Scholar]
- 8.Schachter HM, Pham B, King J, Langford S, Moher D. How efficacious and safe is short-acting methylphenidate for the treatment of attention-deficit disorder in children and adolescents? A meta-analysis. CMAJ. 2001;165:1475–1488. [PMC free article] [PubMed] [Google Scholar]
- 9.Malone MA, Swanson JM. Effects of methylphenidate on impulsive responding in children with attention-deficit hyperactivity disorder. J Child Neurol. 1993;8:157–163. doi: 10.1177/088307389300800209. [DOI] [PubMed] [Google Scholar]
- 10.Rapport MD, Moffitt C. Attention deficit/hyperactivity disorder and methylphenidate. A review of height/weight, cardiovas-cular, and somatic complaint side effects. Clin Psychol Rev. 2002;22:1107–1131. doi: 10.1016/S0272-7358(02)00129-0. [DOI] [PubMed] [Google Scholar]
- 11.Abikoff H, Hechtman L, Klein RG, Weiss G, Fleiss K, Etcovitch J, et al. Symptomatic improvement in children with ADHD treated with long-term methylphenidate and multimodal psychosocial treatment. J Am Acad Child Adolesc Psychiatry. 2004;43:802–811. doi: 10.1097/01.chi.0000128791.10014.ac. [DOI] [PubMed] [Google Scholar]
- 12.Mulholland T. Human EEG, behavioral stillness and biofeedback. Int J Psychophysiol. 1995;19:263–279. doi: 10.1016/0167-8760(95)00019-O. [DOI] [PubMed] [Google Scholar]
- 13.Lofthouse N, Arnold LE, Hersch S, Hurt E, DeBeus R. A review of neurofeedback treatment for pediatric ADHD. J Atten Disord. 2012;16:351–372. doi: 10.1177/1087054711427530. [DOI] [PubMed] [Google Scholar]
- 14.Fuchs T, Birbaumer N, Lutzenberger W, Gruzelier JH, Kaiser J. Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: A comparison with methylphenidate. Appl Psychophysiol Biofeedback. 2003;28:1–12. doi: 10.1023/A:1022353731579. [DOI] [PubMed] [Google Scholar]
- 15.Kropotov J. Quantitative EEG, event-related potentials and neurotherapy. 2010. [Google Scholar]
- 16.Rossiter TR, La Vaque TJ. A comparison of EEG biofeedback and psychostimulants in treating attention deficit/hyperactivity disorders. J Neurother. 1995;1:48–59. doi: 10.1300/J184v01n01_07. [DOI] [Google Scholar]
- 17.Linden M, Habib T, Radojevic V. A controlled study of the effects of EEG biofeedback on cognition and behavior of children with attention deficit disorder and learning disabilities. Biofeedback Self Regul. 1996;21:35–49. doi: 10.1007/BF02214148. [DOI] [PubMed] [Google Scholar]
- 18.Monastra VJ, Monastra DM, George S. The effects of stimulant therapy, EEG biofeedback, and parenting style on the primary symptoms of attention-deficit/hyperactivity disorder. Appl Psychophysiol Biofeedback. 2002;27:231–249. doi: 10.1023/A:1021018700609. [DOI] [PubMed] [Google Scholar]
- 19.Kim HL, Kim JW, Hong SB, Cho JH, Kim BN, Shin MS, et al. Effects of neurofeedback in children with attention deficit hyperactivity disorder: A preliminary study. J Korean Neuropsychiatr Assoc. 2012;51:326–334. doi: 10.4306/jknpa.2012.51.5.326. [DOI] [Google Scholar]
- 20.Yoo J. Effects of neurofeedback training on attention and impulsivity in children with ADHD. Busan National University; Busan: 2009. [Dissertation] [Google Scholar]
- 21.Murray E, Hekler EB, Andersson G, Collins LM, Doherty A, Hollis C, et al. Evaluating digital health interventions: Key questions and approaches. Am J Prev Med. 2016;51:843–851. doi: 10.1016/j.amepre.2016.06.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Busner J, Targum SD. The clinical global impressions scale: Applying a research tool in clinical practice. Psychiatry (Edgmont) 2007;4:28–37. [PMC free article] [PubMed] [Google Scholar]
- 23.Shaffer D, Gould MS, Brasic J, Ambrosini P, Fisher P, Bird H, et al. A children's global assessment scale (CGAS) Arch Gen Psychiatry. 1983;40:1228–1231. doi: 10.1001/archpsyc.1983.01790100074010. [DOI] [PubMed] [Google Scholar]
- 24.DuPaul GJ. Parent and teacher ratings of ADHD symptoms: Psychometric properties in a community-based sample. J Clin Child Adolesc Psychol. 1991;20:245–253. doi: 10.1207/s15374424jccp2003_3. [DOI] [Google Scholar]
- 25.So YK, Noh JS, Kim YS, Ko SG, Koh YJ. The reliability and validity of Korean parent and teacher ADHD rating scale. J Korean Neuropsychiatr Assoc. 2002;41:283–289. [Google Scholar]
- 26.Cho SZ, Chun SY, Hong KE, Shin MS. A study of the development and standardization of ADHD diagnostic system. J Korean Acad Child Adolesc Psychiatry. 2000;11:91–99. [Google Scholar]
- 27.Koo HJ, Shin MS. A standardization study of Children's Color Trails Test (CCTT) J Korean Acad Child Adolesc Psychiatry. 2008;19:28–37. [Google Scholar]
- 28.Shin MS, Park MJ. In: Proceedings of 2006 Conferences of the Korean Psychological Association. The Korean Psychological Association; Seoul: 2006. A standardization study for Korean version of the Stroop color-word test children's version; pp. 628–629. [Google Scholar]
- 29.Heinrich H, Gevensleben H, Freisleder FJ, Moll GH, Rothenberger A. Training of slow cortical potentials in attention-deficit/hyperactivity disorder: Evidence for positive behavioral and neurophysiological effects. Biol Psychiatry. 2004;55:772–775. doi: 10.1016/j.biopsych.2003.11.013. [DOI] [PubMed] [Google Scholar]
- 30.Bakhshayesh AR, Hänsch S, Wyschkon A, Rezai MJ, Esser G. Neurofeedback in ADHD: A single-blind randomized controlled trial. Eur Child Adolesc Psychiatry. 2011;20:481–491. doi: 10.1007/s00787-011-0208-y. [DOI] [PubMed] [Google Scholar]
- 31.Boot WR, Simons DJ, Stothart C, Stutts C. The pervasive problem with placebos in psychology: Why active control groups are not sufficient to rule out placebo effects. Perspect Psychol Sci. 2013;8:445–454. doi: 10.1177/1745691613491271. [DOI] [PubMed] [Google Scholar]
- 32.Granic I, Lobel A, Engels RC. The benefits of playing video games. Am Psychol. 2014;69:66–78. doi: 10.1037/a0034857. [DOI] [PubMed] [Google Scholar]