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Published on in Vol 6, No 7 (2022): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/35636, first published .
Preliminary Real-World Evidence Supporting the Efficacy of a Remote Neurofeedback System in Improving Mental Health: Retrospective Single-Group Pretest-Posttest Study

Preliminary Real-World Evidence Supporting the Efficacy of a Remote Neurofeedback System in Improving Mental Health: Retrospective Single-Group Pretest-Posttest Study

Preliminary Real-World Evidence Supporting the Efficacy of a Remote Neurofeedback System in Improving Mental Health: Retrospective Single-Group Pretest-Posttest Study

Authors of this article:

Jocelyne C Whitehead1, 2 Author Orcid Image ;   Ron Neeman2 Author Orcid Image ;   Glen M Doniger2 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

    Original Paper

    1Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada

    2Myndlift Ltd, Tel Aviv, Israel

    Corresponding Author:

    Jocelyne C Whitehead, PhD

    Integrated Program in Neuroscience

    McGill University

    845 Rue Sherbrooke O

    Montreal, QC, H3A 0G4

    Canada

    Phone: 1 866 994 3210

    Email: jocelyne.whitehead@mail.mcgill.ca


    Background: Neurofeedback training (NFT) has been shown to be effective in treating several disorders (eg, attention-deficit/hyperactivity disorder [ADHD], anxiety, and depression); however, little is currently known regarding the effectiveness of remote NFT systems.

    Objective: This retrospective study provides real-world data (N=593) to assess the efficacy of app-based remote NFT in improving brain health and cognitive performance.

    Methods: Improvement was measured from pre- to postintervention of in-app assessments that included validated symptom questionnaires (the 12-item General Health Questionnaire, the ADHD Rating Scale IV, the Adult ADHD Self-Report Scale, the 7-item Generalized Anxiety Disorder scale, and the 9-item Patient Health Questionnaire), a cognitive test of attention and executive functioning (ie, continuous performance task), and resting electroencephalography (EEG) markers. Clinically significant improvement was evaluated using standard approaches.

    Results: The greatest improvement was reported for the anxiety questionnaire, for which 69% (68/99) of participants moved from abnormal to healthy score ranges. Overall, adult and child participants who engaged in neurofeedback to improve attention and executive functions demonstrated improved ADHD scores and enhanced performance on a cognitive (ie, response inhibition) task. Adults with ADHD additionally demonstrated elevated delta/alpha and theta/alpha ratios at baseline and a reduction in the delta/alpha ratio indicator following neurofeedback.

    Conclusions: Preliminary findings suggest the efficacy of app-based remote neurofeedback in improving mental health, given the reduced symptom severity from pre- to postassessment for general psychological health, ADHD, anxiety, and depression, as well as adjusted resting EEG neural markers for individuals with symptoms of ADHD. Collectively, this supports the utility of the in-app assessment in monitoring behavioral and neural indices of mental health.

    JMIR Form Res 2022;6(7):e35636

    doi:10.2196/35636

    Keywords

    EEG biofeedback; remote care; neurofeedback; attention-deficit/hyperactivity disorder; delta/alpha ratio 


    Background

    Neurofeedback training (NFT) is considered a primary or supplementary treatment for a number of disorders, including attention-deficit/hyperactivity disorder (ADHD) [1-5], anxiety [6-9], and depression [7,8,10]. The American Academy of Pediatrics [11] provided a “level 1 best support” rating of NFT as a safe and effective evidence-based therapy for childhood ADHD. Nonetheless, several significant barriers prevent patients from receiving quality neurofeedback therapeutics; for example, electroencephalography (EEG) systems are expensive, complex, and often only accessible at health care clinics. A recent pilot study [12] provided encouraging evidence for the efficacy of therapist-guided NFT, suitable for remote home-based use. Findings showed improved ADHD symptomatology in a small cohort of children after 9 weeks of NFT. The system was designed as an affordable convenient wireless alternative to clinic-based EEG. NFT users regulate neural activity through operant conditioning, which can lead to morphological changes in the brain [13,14] and calmer, more focused cognitive, affective, and physical functioning. Currently, little is known about the effectiveness of NFT systems in the field [15]; therefore, this retrospective open-label pilot study offers real-world data supporting the efficacy of remote NFT in improving brain health.

    Mental Health Improvement in Real-World Settings

    Unlike standard EEG systems, Myndlift is an easy-to-use tool for patients and clinicians (Figure 1). While wearing the validated EEG headband (Muse; InteraXon [16,17]) containing four dry recording electrodes (ie, anterior frontal [AF] 7, AF8, temporal pole [TP] 9, and TP10), one ground electrode, and one auxiliary wet electrode, the patient trains with an Android or iOS app linked to the headset by Bluetooth, which delivers visual and auditory feedback during YouTube videos or specialized games. When patients’ brain waves are in the desired range, positive feedback is delivered. A therapist can set or adjust the training protocol and monitor progress remotely via a cloud-based web service. The device incorporates an app-based assessment, lasting approximately 40 minutes, completed prior to NFT (ie, baseline) and periodically over the intervention period for longitudinal tracking of improvement.

    ‎
    Figure 1. Myndlift in-app assessment screens. From left to right: introduction, symptom questionnaires, resting electroencephalography assessment, and cognitive task.
    View this figure

    Real-world studies provide external validity and accurately represent the heterogeneity of a patient population [18]. From the app, real-world data were collected from more than 500 participants on outcome measures, including pre- and postintervention assessments of validated symptom questionnaires, a cognitive test of attention and executive functioning (ie, continuous performance task [CPT]), and resting EEG markers. An efficacious system could serve as a reliable, cost-effective solution for users. In-clinic NFT costs approximately US $150 to $200 per session, with a minimum of 30 to 40 sessions typically recommended. In contrast to a cost-per-session model, remote NFT could offer monthly charges, ranging from US $200 to $500.

    EEG Neuromarkers of ADHD

    Given the success of neurofeedback for child ADHD, more adults with ADHD are turning to NFT for treatment. Currently, 6.76% of adults worldwide—translating to 366.3 million people—are affected [19]. ADHD is commonly recognized as a hypoaroused brain state [20]. In recent years, EEG measures have provided supporting evidence for popular theoretical models of hypoactivation [21] related to core symptoms of hyperactivity, inattention, and impulsivity [22]. The hypoarousal state is best localized to frontal and posterior regions [23] (ie, neuroanatomical structures subserving attentional networks [24,25]). EEG patterns of ADHD in children are characterized by elevated low-frequency power (ie, primarily theta) and reduced relative high-frequency power (ie, alpha and beta) [23,26-28], or an elevated ratio of the two (ie, low to high frequency). The theta/beta ratio (TBR) is the most common form of NFT in treating ADHD [29,30]; however, inconsistencies in the literature suggest that TBR [31,32] may not be reliable as a diagnostic measure [33]. This may reflect EEG heterogeneity across ADHD-diagnosed individuals (eg, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5] subtypes; psychiatric comorbidities; age; and sex) [34,35]. For instance, although theta and beta power differences are evident in child ADHD [31,36], a recent review [37] suggested that theta and alpha frequencies may be more reliable markers for adults. Notably, most adult studies emphasize group differences in alpha power during eyes-closed conditions [38-43], while more recent work has identified elevated theta and delta power in adults with ADHD [44,45]. Given this evidence, this study investigates whether TBR versus the delta/alpha ratio (DAR) or the theta/alpha ratio (TAR) are biomarkers for adult ADHD. Overall, the study evaluates evidence for improvement in mental health via symptom questionnaires, a CPT, and hypothesized EEG markers. Findings have implications for the benefits of NFT and efficacy of a remote home-based system.


    Participants

    Participants, 13 years of age or older, signed up through their clinician or a clinician suggested by Myndlift and completed NFT at home or in clinic in a clinical care context. Informed consent was provided through the app, allowing participants’ anonymized data to be used for research. Data were included for analysis if baseline (ie, preintervention) assessment was conducted after 7 or fewer NFT sessions (ie, attributed to in-app NFT tutorial). For analyses of improvement, postintervention sessions occurred 30 to 180 days after baseline with 20 or more NFT sessions completed [46]. An average of 1 or more NFT sessions per week was required for inclusion, given that effective neurofeedback requires consistency [47,48], irrespective of the neurofeedback protocol used. Data were collected via the app.

    Ethical Considerations

    Procedures were reviewed by an independent Institutional Review Board (IRB)—Pearl IRB—who permitted IRB exemption for analyses of data previously collected and deidentified, following the guidelines of the Declaration of Helsinki.

    Neurofeedback Protocol

    Participants performed neurofeedback protocols (Multimedia Appendix 1) that were customized by their clinicians and consistent with current literature [49].

    Procedure and Outcome Measures

    Symptom Questionnaires

    The in-app assessment includes 14 brief standardized questionnaires commonly used to screen for mental health conditions. In this study, data were reported for the following five questionnaires completed at baseline and follow-up by at least 25 participants: the 12-item General Health Questionnaire (GHQ-12) [50], the ADHD Rating Scale IV (ADHD-RS-IV) [51], the Adult ADHD Self-Report Scale (ASRS) for DSM-5 [52], the 7-item Generalized Anxiety Disorder scale (GAD-7) [53], and the 9-item Patient Health Questionnaire (PHQ-9) [54]. For each questionnaire, participants filled out self-report measures based on frequency of symptom occurrence using a 4- or 5-point Likert-style scale. Total scores were calculated for use in improvement analyses. Participants engaging in neurofeedback for ADHD completed the ASRS [52] if they were 18 years of age or older; otherwise, they completed the ADHD-RS-IV. The GHQ-12, GAD-7, and PHQ-9 were completed by participants of all ages [55-58].

    Continuous Performance Task

    The assessment contained an 8-minute CPT, a behavioral test of response inhibition, in which participants are instructed to tap the screen when the target object (ie, an arrow-like shape pointing upward) is shown, but not when other stimuli appear. The interstimulus interval and presence of audiovisual distracter stimuli were varied throughout the task. Outcomes included average response time (RT) and response time variability (ie, the SD of RT [SDRT]), as well as omission and commission errors related to inattention and impulsivity, respectively [59]. This type of test is commonly used as an objective measure of attention and executive function [60-62] and has become a standard assessment tool for attentional difficulties [59,63,64].

    Resting EEG

    Resting EEG was recorded from 9 electrodes (ie, AF7, AF8, TP9, TP10, central [C] zero [z], frontal [F] z, F3, F4, and occipital [O] 1). The EEG assessment was divided into five sequential (ie, “sensing”) phases; in each phase, the auxiliary electrode was placed at a different scalp location: central (Cz), frontal (Fz), left (F3), right (F4), and back or posterior (O1). Each phase was split into eyes-closed and eyes-open blocks. A block continued until 30 seconds of clean EEG—sampled at 256 Hz—had been recorded, which typically took up to 45 seconds.

    Statistical Analysis

    Symptom Questionnaires

    Questionnaire results were analyzed in terms of improvement in total score from pre- to postintervention, including mean change in points, effect size (ie, Cohen d), and percent of users with clinically significant improvement, defined as 20% improvement [65,66]. Results are presented separately for participants scoring in healthy and abnormal ranges at baseline, as per conventional clinical cutoff values. The percent of participants who shifted from abnormal to normal (ie, healthy) ranges after the intervention is also reported. Paired-samples t tests (2-tailed) evaluated statistically significant improvement for each clinical measure (P<.05). By convention, small, medium, and large effects correspond to d=0.2, d=0.5, and d=0.8, respectively. For symptom questionnaires, CPT, and resting EEG analyses, multiple comparisons were corrected using the Benjamini-Hochberg (BH) method [67] to maintain a family-wise error at P=.05, reported as BH-adjusted P values (PBH). The Levene test assessed assumptions of equality of variance and corrected for inhomogeneities.

    Continuous Performance Task

    CPT results were analyzed for participants who completed child (ie, ADHD-RS-IV) or adult (ie, ASRS) ADHD questionnaires. Results are given in terms of improvement in RT and SDRT for correct responses (ie, shorter and less variable response times, respectively), commission errors, and omission errors. This includes mean change, in milliseconds or errors, and effect size. RT and SDRT scores were standardized by age to minimize age effects on performance [68]. Percent of participants demonstrating clinically significant improvement was reported, defined by a reliable change index (RCI) [69] that accounts for practice effects [70]. Exceeding a critical value of 95% for a 1-tailed test—equivalent to 1.65 SD units on a standardized z scale—indicates a significant reliable change, similar to others [71].

    Resting EEG

    Participants who completed the adult ADHD questionnaire at baseline were split into groups with “healthy” and “abnormal” ranges of values based on their score. Only participants with clean EEG signals were included (see Multimedia Appendix 2 for EEG preprocessing). Results were reported in terms of EEG amplitude (ie, Hz; relative power) for TAR, DAR, and TBR at baseline. Independent-samples t tests were conducted for each power ratio across groups (ie, healthy and abnormal values). Frequency bands were defined as follows: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz). These were averaged across frontal electrodes (ie, F3 and F4, based on the frontal nodes of the frontoparietal network [25,72] and the prevalence of a clean EEG signal) during the eyes-closed condition. Improvement analyses were conducted separately for each group and included the mean change in ratio amplitude from pre- to postintervention and associated effect size; paired-samples t tests were used to evaluate within-group changes.


    Sample Characteristics

    Data from 560 participants met the criteria for inclusion in the analysis. Depending on clinical considerations determined by their therapist, subsets of participants completed each symptom questionnaire, CPT, resting EEG, or any combination of the three. Table 1 gives sample characteristics for each assessment component, including the NFT protocols completed by 50% or more of each sample population (Multimedia Appendix 1).

    Table 1. Sample characteristics as separated by each outcome measure and analysis.
    MeasureAge (years)Gender, n (%)Test setting, n (%)NFTa protocols used in ≥50% of sampleNo. of sessions, mean (SD)Treatment duration (days), mean (SD)Frequency (sessions/wk), mean (SD)

    		Mean (SD)RangeFemaleMaleClinicHome



    Symptom questionnaire pre-post (n=301b)38 (14.5)13-71157 (52.7)141 (47.3)220 (73.1)81 (26.9)Reduce theta;
    reduce high beta;
    enhance low beta    		53 (38.2)91 (41.0)4 (2.6)
    CPTc pre-post (ADHDd; n=203e)37 (12.9)13-69103 (51.2)98 (48.8)112 (55.2)91 (44.8)Reduce theta; reduce high beta53 (29.5)96 (42.2)4 (1.9)
    Resting EEGf baseline (adult ADHD; n=271g)38 (10.9)18-7094 (35.2)173 (64.8)87 (32.1)184 (67.9)N/AhN/AN/AN/A
    Resting EEG pre-post (adult ADHD; n=41i)36 (9.3)19-5517 (42.5)23 (57.5)5 (12.2)36 (87.8)Reduce theta;
    enhance alpha;
    reduce high beta    		55 (30.8)76 (27.0)5 (2.2)

    aNFT: neurofeedback training.

    bNo gender identity was reported by 3 participants (n=298).

    cCPT: continuous performance task.

    dADHD: attention-deficit/hyperactivity disorder.

    eNo gender identity was reported by 2 participants (n=201).

    fEEG: electroencephalography.

    gNo gender identity was reported by 4 participants (n=267).

    hN/A: not applicable; intervention details were not reported, as only preintervention values were of interest for baseline analyses.

    iNo gender identity was reported by 1 participant (n=40).

    Symptom Questionnaires

    Results for participants who completed symptom questionnaires (n=301) were separated into groups with abnormal and healthy scores (Table 2). Most participants engaged in NFT protocols to reduce theta (227/301, 75.4%) and enhance high beta (248/301, 82.4%), while many who completed the PHQ-9 (76/134, 56.7%) and the ASRS (59/112, 52.7%) also performed enhanced alpha, whereas children who completed the ADHD-RS-IV also often included enhanced low beta (21/27, 78%) and enhanced sensorimotor rhythm (SMR; 16/27, 59%). In the groups with abnormal results, all questionnaires had large effect sizes (d=0.99 to 2.41), while the effect sizes for groups with healthy results were large only for child and adult ADHD questionnaires. Improvement in the groups with abnormal results was statistically significant for all questionnaires, with the majority (30/56, 54% to 7/7, 100%) of users demonstrating clinically significant change (ie, ≥20%) [65,66]. The most prominent improvement was observed in participants with abnormal baseline anxiety or child ADHD scores. Nevertheless, ADHD-RS-IV findings are considered preliminary given the small sample size. Most participants (30/56, 54% to 7/7, 100%) in the groups with abnormal results shifted their values to healthy ranges at postintervention. Improvement of healthy participants was statistically significant for all questionnaires, with the majority (30/66, 45% to 14/20, 70%) demonstrating clinically significant change.

    Table 2. Improvement in self-reported subjective symptoms after ≥30 days of Myndlift neurofeedback for users that scored in the healthy range, and separately for those that scored in the abnormal range (per conventional clinical cutoffs) at baseline.
    Questionnaire and group at baseline (cutoff value)No. of sessions, mean (SD)Treatment duration (days), mean (SD)Change (points decreased), mean (SD)Change T valueChange P valueaEffect size, dUsers improved by ≥20%, n (%)Abnormal to healthy results, n (%)
    12-item General Health Questionnaire (maximum score = 36)

    		Abnormal
    (≥12; n=197)    		53 (39.1)94 (42.2)7.8 (7.80)13.94<.0010.99139 (71)113 (57)
    Healthy (12; n=66)52 (34.3)84 (36.6)1.0 (4.28)1.90.060.2330 (45)N/Ab
    ADHDc Rating Scale IV (for children; maximum score = 54): preliminary
    Abnormal (>36; n=7)49 (19.7)75 (32.8)19.3 (7.99)6.38.0012.417 (100)7 (100)
    Healthy (≤36; n=20)53 (23.2)102 (37.9)7.9 (8.10)4.36.0010.9814 (70)N/A
    Adult ADHD Self-Report Scale (maximum score =24)

    		Abnormal
    (≥14; n=56)    		48 (25.6)86 (41.5)4.0 (3.81)7.83.0011.0530 (54)30 (54)
    Healthy (14; n=56)63 (35.7)97 (37.2)2.1 (2.14)7.38.0010.9933 (59)N/A
    7-item Generalized Anxiety Disorder scale (maximum score = 21)

    		Abnormal
    (≥14; n=99)    		52 (36.7)87 (40.2)6.4 (5.18)12.39.0011.2482 (83)68 (69)
    Healthy (14; n=107)55 (32.5)97 (40.4)1.3 (3.92)3.43.0010.3363 (59)N/A
    9-item Patient Health Questionnaire (maximum score =27)

    		Abnormal
    (≥10; n=63)    		57 (47.6)88 (37.7)6.2 (5.47)8.94.0011.1345 (71)38 (60)
    Healthy (10; n=71)57 (34.7)95 (39.8)1.5 (4.07)3.04.0040.3649 (69)N/A

    aReported as Benjamini-Hochberg–adjusted P values.

    bN/A: not applicable; healthy subjects are already within the healthy range.

    cADHD: attention-deficit/hyperactivity disorder.

    Continuous Performance Task

    Participants completing CPT and ADHD questionnaires performed primarily reduced theta (76/99, 77%) and enhanced high beta (81/99, 90%) protocols. Most adults also performed enhanced alpha (54/90, 60%), whereas most children also performed enhanced low beta (9/9, 100%) and enhanced SMR (7/9, 78%). Results (n=99) for average RT, SDRT, omission errors, and commission errors were divided by abnormal versus healthy scores for child and adult ADHD combined (Table 3). The greatest improvement observed, irrespective of group (ie, abnormal and healthy ADHD ranges), was in SDRT (d=1.02 and d=1.24, respectively), where nearly half of the participants (42/99, 43%) demonstrated clinically significant improvement, as indicated by the RCI. Although average RTs improved comparably (42/99, 43%), differences between pre- and postintervention were significant only for the healthy results group (d=0.56). At least one-third of users improved in their commission errors (35/99, 35%) and omission errors (45/99, 45%) from pre- to postintervention. Results from a group (n=104) with unknown ADHD assignment were comparable to those of groups with abnormal and healthy results (Multimedia Appendix 3).

    Table 3. Improvement in CPT after ≥30 days of Myndlift neurofeedback (n=99) separately for healthy users that scored in the normal range for children or adults at baseline and for those in the abnormal ADHD range (per conventional clinical cutoffs).
    CPTa outcome and group results at baseline ASRSb or ADHDc-RS-IVdNo. of sessions, mean (SD)Treatment duration (days), mean (SD)Change reduction, mean (SD)eChange T valueChange P valuefEffect size, dUsers improved (RCIg ≥1.65 SD), n (%)
    Average response time

    		Abnormal (n=46)48 (26.3)85 (40.3)8.9 (33.52)1.80.080.2720 (43)
    Healthy (n=53)61 (31.0)100 (40.0)15.0 (26.96)4.05.0010.5622 (42)
    Response time variability (SD of response time)

    		Abnormal (n=46)48 (26.3)85 (40.3)10.3 (10.02)6.95.0011.0218 (39)
    Healthy (n=53)61 (31.0)100 (40.0)10.7 (8.64)8.99.0011.2425 (47)
    Commission errors (impulsivity)

    		Abnormal (n=46)48 (26.3)85 (40.3)4.0 (7.23)3.75.0010.5519 (41)
    Healthy (n=53)61 (31.0)100 (40.0)2.0 (3.17)4.51.0010.6216 (30)
    Omission errors (inattention)

    		Abnormal (n=46)48 (26.3)85 (40.3)1.5 (3.16)3.27.0030.4824 (52)
    Healthy (n=53)61 (31.0)100 (40.0)0.64 (1.88)2.48.020.3421 (40)

    aCPT: continuous performance task.

    bASRS: Adult ADHD Self-Report Scale.

    cADHD: attention-deficit/hyperactivity disorder.

    dADHD-RS-IV: ADHD Rating Scale IV.

    eReported in milliseconds for response time average and variability, and in number of errors for commission and omission errors.

    fReported as Benjamini-Hochberg–adjusted P values.

    gRCI: reliable change index.

    EEG Indicators of Adult ADHD

    Resting EEG Baseline

    The DAR, TAR, and TBR were calculated from baseline resting EEG data (n=271) in frontal regions (ie, average of F3 and F4) with eyes closed from participants scoring in abnormal (n=125) or healthy ranges (n=146) on the adult ADHD questionnaire.

    Regarding the DAR, an independent-samples t test demonstrated that participants in the abnormal results group (mean 1.10, SD 0.61) had significantly greater frontal DAR than healthy participants (mean 0.90, SD 0.48; t235=3.02, PBH=.009, d=0.37). The Levene test indicated unequal variances (F=5.25, P=.02), so degrees of freedom were adjusted from 269 to 235. Post hoc independent-samples t tests confirmed that results were driven by participants in the abnormal results group having significantly greater frontal delta power (t269=2.80, PBH=.01, d=0.34) and less frontal alpha power (t269=2.61, PBH=.01, d=0.34) than healthy participants.

    Regarding the TAR, a comparable t test reported a significant difference for the frontal TAR (t269=2.46, PBH=.02, d=0.30), as participants with abnormal scores (mean 0.64, SD 0.30) had significantly greater ratios than those with healthy scores (mean 0.56, SD 0.26). Post hoc t tests confirmed that results were driven by less frontal alpha, as opposed to differences in theta (t269=1.11, PBH=.27, d=0.13).

    Regarding the TBR, a final t test reported no significant difference between participants with abnormal scores (mean 0.66, SD 0.27) and those with healthy scores (mean 0.64, SD 0.31; t269=0.532, PBH=.60, d=0.06).

    Preliminary Resting EEG Improvement

    Changes in the DAR, TAR, and TBR in the frontal regions with eyes closed were reported for participants (n=41) scoring in the abnormal (n=20) or healthy ranges (n=21) of the adult ADHD questionnaire (Table 4). Most participants completed reduced theta (32/41, 78%), enhanced high beta (37/41, 90%), and enhanced alpha protocols (27/41, 66%). After correcting for multiple comparisons, significant improvement was only reported for the DAR in the abnormal results group.

    Table 4. Change in resting EEG ratios from frontal (ie, average F3 and F4) electrodes during the eyes-closed condition after ≥30 days of Myndlift neurofeedback (n=41) for healthy users and separately for those that scored in the abnormal adult ADHD range (per conventional clinical cutoffs) at baseline.
    EEGa pre-post outcome and group at baseline (cutoff value)No. of sessions, mean (SD)Treatment duration (days), mean (SD)Change reduction (Hz), mean (SD)Change T valueChange P valuebEffect size, d
    Delta/alpha ratio

    		Abnormal (≥14; n=20)49 (22.1)77 (26.8)0.20 (0.284)3.15.030.70

    		Healthy (14; n=21)61 (36.9)76 (27.8)0.08 (0.450)0.79.590.18
    Theta/alpha ratio

    		Abnormal (≥14; n=20)49 (22.1)77 (26.8)0.04 (0.171)1.00.660.22

    		Healthy (14; n=21)61 (36.9)76 (27.8)0.01 (0.227)0.35.660.08
    Theta/beta ratio

    		Abnormal (≥14; n=20)49 (22.1)77 (26.8)0.04 (0.144)1.34.790.30

    		Healthy (14; n=21)61 (36.9)76 (27.8)0.02 (0.218)0.44.730.10

    aEEG: electroencephalography.

    bReported as Benjamini-Hochberg–adjusted P values.


    Principal Findings

    This retrospective study offers initial evidence of therapist-guided remote neurofeedback as an effective tool for reducing subjective symptoms, improving objective cognitive performance, and adaptively modifying EEG markers. Improvements in attention were evident in children and adults with ADHD, as well as healthy participants. Findings suggest that the TBR is not a reliable marker for adult ADHD, instead demonstrating alternative elevated slow/fast power ratios [37]. Moreover, we provide preliminary evidence for improvement (ie, reduced DAR) in adults with ADHD. These findings offer a promising use for remote NFT as a low-cost alternative to clinic-based EEG.

    Efficacy for Improving Mental Health Remotely

    Based on real-world data, significant improvement was reported across standardized questionnaires. The greatest improvement was observed in participants with abnormal anxiety scores, where most received reduced theta, enhanced high beta, and enhanced alpha protocols. As anticipated, greater effect sizes were observed for participants with scores in the abnormal versus healthy ranges. Interestingly, healthy participants and those with ADHD, both children and adults, demonstrated significant improvement with large effect sizes after completing primarily reduced theta and enhanced high beta protocols, as well as adults who completed enhanced alpha protocols or children who completed reduced low beta and reduced SMR protocols. Consistent with the literature [33-35], our findings suggest that children and adults may benefit from unique NFT protocols to improve ADHD symptoms, although a larger sample is required to confirm preliminary ADHD-RS-IV results.

    Apart from the child ADHD assessment, questionnaire analyses included large total sample numbers (ie, 112 to 263 participants), and after an average of 53 NFT sessions, 57% to 78% of the participants demonstrated significant improvement, depending on the questionnaire. Results were particularly impressive compared to other in-app mental health therapeutics [73-76], such as mobile-enabled text psychotherapy [77] or app-based cognitive behavioral therapy [78]. The majority (61%) of participants scoring in the abnormal ranges moved to the healthy results group over an average of approximately 3 months, a time frame costing less than US $1500 with Myndlift versus US $6000 to $8000 for traditional neurofeedback.

    Improved Cognitive Performance for Healthy Participants and Those With ADHD

    NFT led to greater consistency in response times on a response inhibition task for subjects scoring in healthy or abnormal ADHD ranges, agreeing with similar reports of subjects with ADHD [79,80]. In addition, the RCI demonstrated that approximately 50% of healthy participants improved their average response time, while similarly, participants in abnormal ranges reduced omission errors. Importantly, CPT findings agree with improved ADHD questionnaire scores, suggesting that NFT provides objective evidence of improved executive function, the primary cognitive domain impacted by attentional difficulties.

    Identifying Adult ADHD Neuromarkers

    Resting EEG findings demonstrated that elevated DAR and TAR were indicative of adult ADHD at baseline. This translated to significantly higher levels of delta and lower levels of alpha, as previously reported in adults with ADHD [39-42,81,82]. Notably, Liechti and colleagues [35] reported high theta to be less consistent in adults than in children, and that ADHD versus healthy control classification improved having exploratorily included delta waves in the discriminant analysis. Adults with ADHD may present slower theta waves—bordering fast delta waves—than children, although further analysis is required. Together, findings are consistent with the cortical hypoarousal theory, where low-power fast oscillations accompany reduced self-control and executive functioning [83], and high-power slow oscillations are reported with decreased subcortical motivational drive [84]. Preliminary evidence for reduced DAR in adult ADHD from pre- to postassessment may reflect the improved ADHD symptoms and CPT measures, particularly given the success of protocols inhibiting slow oscillations and enhancing fast oscillation [1], and the high percentage of ADHD participants performing reduced theta (ie, slow) and enhanced alpha (ie, fast) protocols.

    In contrast to our work and that of others, several groups reported high alpha power at baseline during eyes-closed conditions in adult ADHD populations [85,86], or rather, no difference across ADHD participants and healthy controls [87,88]. Importantly, variability across the adult ADHD literature may, in part, be due to the heterogeneity of ADHD [34,35] and differences in study designs, sample sizes, analyses, and EEG technology [89]. For example, Loo and colleagues [38] demonstrated that adults with ADHD combined-type (ie, symptoms of inattention and hyperactivity or impulsivity) present reduced alpha power globally, compared to ADHD inattentive-type or non-ADHD controls.

    Limitations and Future Directions

    Study results are encouraging, but conclusions should be tempered by limitations, including small subgroup sample sizes and lack of control groups. Moreover, subjects may have received alternative treatment in parallel (eg, medication) that could influence symptom improvement as well as alter neuromarkers. For example, two studies administering stimulants (ie, methylphenidate or dexamphetamine) to treat symptoms of ADHD in adults demonstrated altered delta [90,91] and theta waves [90] posttreatment. No changes in alpha or beta waves were reported. Given the evidence in this study for altered delta and alpha waves in adults with abnormal ADHD scores, we would hypothesize that the mechanism of action for stimulants versus NFT may differ, resulting in influence over varied frequency bands. Moreover, as this population reflects real-world use, the likelihood of these two forms of treatment to have commenced simultaneously, for treating symptoms of depression, anxiety, and ADHD, would arguably be low. Those seeking treatment with remote neurofeedback most often do so to avoid taking pharmaceuticals [92,93] or, rather, to supplement their current treatment, which alone may not be sufficiently effective [94]. Frank H Duffy [95], a Harvard professor and pediatric neurologist, suggests that “if any medication had demonstrated such a wide spectrum of efficacy it would be universally accepted and widely used.” Further, controlled research studies will be required to facilitate comparison of neurofeedback efficacy with other interventions. Notwithstanding these limitations, the findings are essential as they reflect real-world benefits of remote neurofeedback to actual patients. Follow-up analyses will compare benefits across NFT protocols and will further evaluate the impact on resting EEG outcomes.

    Conclusions

    Preliminary findings from this retrospective pilot study demonstrate efficacy of remote NFT in improving mental health, particularly for individuals with symptoms of ADHD and anxiety, mainly through reduced theta, enhanced high beta, and enhanced alpha NFT protocols. Moreover, adult ADHD was distinguished from healthy individuals by elevated frontal DARs, where ratios were significantly reduced following NFT. The effectiveness of the system in a real-world population via remote use positions it as an affordable and accessible alternative to clinic-based systems.

    Acknowledgments

    Funding for this study was provided by McGill University, Montreal, Canada, and Myndlift Ltd. JCW received funding from McGill University for a doctoral internship.

    Authors' Contributions

    JCW conducted the analyses, interpreted the results, cowrote the paper, and revised the final version. RN co-designed the study, extracted the data, interpreted the results, cowrote the paper, and revised the final version. GMD designed the study, cowrote the paper, and revised the final draft. All authors reviewed and revised the results and approved the final version of the paper.

    Conflicts of Interest

    JCW is a freelance consultant for Myndlift. RN and GMD are employees of Myndlift.

    ‎
    Multimedia Appendix 1

    Neurofeedback protocols implemented in this study.

    DOCX File , 18 KB
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    Multimedia Appendix 2

    Electroencephalography preprocessing.

    DOCX File , 13 KB
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    Multimedia Appendix 3

    Continuous performance task improvement for users without an ADHD assessment. ADHD: attention-deficit/hyperactivity disorder.

    DOCX File , 14 KB
    1. Arns M, de Ridder S, Strehl U, Breteler M, Coenen A. Efficacy of neurofeedback treatment in ADHD: The effects on inattention, impulsivity and hyperactivity: A meta-analysis. Clin EEG Neurosci 2009 Jul;40(3):180-189. [CrossRef] [Medline]
    2. Micoulaud-Franchi J, Geoffroy PA, Fond G, Lopez R, Bioulac S, Philip P. EEG neurofeedback treatments in children with ADHD: An updated meta-analysis of randomized controlled trials. Front Hum Neurosci 2014;8:906 [FREE Full text] [CrossRef] [Medline]
    3. Bussalb A, Congedo M, Barthélemy Q, Ojeda D, Acquaviva E, Delorme R, et al. Clinical and experimental factors influencing the efficacy of neurofeedback in ADHD: A meta-analysis. Front Psychiatry 2019;10:35 [FREE Full text] [CrossRef] [Medline]
    4. Riesco-Matías P, Yela-Bernabé JR, Crego A, Sánchez-Zaballos E. What do meta-analyses have to say about the efficacy of neurofeedback applied to children with ADHD? Review of previous meta-analyses and a new meta-analysis. J Atten Disord 2021 Feb;25(4):473-485. [CrossRef] [Medline]
    5. Lambez B, Harwood-Gross A, Golumbic EZ, Rassovsky Y. Non-pharmacological interventions for cognitive difficulties in ADHD: A systematic review and meta-analysis. J Psychiatr Res 2020 Jan;120:40-55. [CrossRef] [Medline]
    6. Moore NC. A review of EEG biofeedback treatment of anxiety disorders. Clin Electroencephalogr 2000 Jan;31(1):1-6. [CrossRef] [Medline]
    7. Hammond DC. Neurofeedback treatment of depression and anxiety. J Adult Dev 2005 Aug;12(2-3):131-137. [CrossRef]
    8. Hammond DC. Neurofeedback with anxiety and affective disorders. Child Adolesc Psychiatr Clin N Am 2005 Jan;14(1):105-123, vii. [CrossRef] [Medline]
    9. Tolin D, Davies C, Moskow D, Hofmann S. Biofeedback and neurofeedback for anxiety disorders: A quantitative and qualitative systematic review. Adv Exp Med Biol 2020;1191:265-289. [CrossRef] [Medline]
    10. Micoulaud-Franchi J, McGonigal A, Lopez R, Daudet C, Kotwas I, Bartolomei F. Electroencephalographic neurofeedback: Level of evidence in mental and brain disorders and suggestions for good clinical practice. Neurophysiol Clin 2015 Dec;45(6):423-433. [CrossRef] [Medline]
    11. American Academy of Pediatrics. Appendix S2: Evidence-based child and adolescent psychosocial interventions. Pediatrics 2010;125:S128 [FREE Full text] [CrossRef]
    12. Doniger GM, Kaddan A. Pilot study of the efficacy of mobile neurofeedback for attention-deficit/hyperactivity disorder (ADHD). In: Proceedings of the 27th ISNR Conference: Keynotes, Plenary Sessions, and Poster and Presentations. 2019 Presented at: The 27th ISNR Conference; September 19-22, 2019; Denver, CO p. 220   URL: https://www.neuroregulation.org/article/view/19901/13353
    13. Marins T, Rodrigues E, Bortolini T, Melo B, Moll J, Tovar-Moll F. Structural and functional connectivity changes in response to short-term neurofeedback training with motor imagery. Neuroimage 2019 Jul 01;194:283-290. [CrossRef] [Medline]
    14. Ghaziri J, Tucholka A, Larue V, Blanchette-Sylvestre M, Reyburn G, Gilbert G, et al. Neurofeedback training induces changes in white and gray matter. Clin EEG Neurosci 2013 Oct;44(4):265-272. [CrossRef] [Medline]
    15. Antle AN, Chesick L, Sridharan SK, Cramer E. East meets west: A mobile brain-computer system that helps children living in poverty learn to self-regulate. Pers Ubiquitous Comput 2018 Jun 12;22(4):839-866. [CrossRef]
    16. Krigolson OE, Hammerstrom MR, Abimbola W, Trska R, Wright BW, Hecker KG, et al. Using Muse: Rapid mobile assessment of brain performance. Front Neurosci 2021;15:634147 [FREE Full text] [CrossRef] [Medline]
    17. Krigolson OE, Williams CC, Norton A, Hassall CD, Colino FL. Choosing MUSE: Validation of a low-cost, portable EEG system for ERP research. Front Neurosci 2017;11:109 [FREE Full text] [CrossRef] [Medline]
    18. Blonde L, Khunti K, Harris SB, Meizinger C, Skolnik NS. Interpretation and impact of real-world clinical data for the practicing clinician. Adv Ther 2018 Nov;35(11):1763-1774 [FREE Full text] [CrossRef] [Medline]
    19. Song P, Zha M, Yang Q, Zhang Y, Li X, Rudan I. The prevalence of adult attention-deficit hyperactivity disorder: A global systematic review and meta-analysis. J Glob Health 2021 Feb 11;11:04009 [FREE Full text] [CrossRef] [Medline]
    20. Satterfield JH, Dawson ME. Electrodermal correlates of hyperactivity in children. Psychophysiology 1971 Mar;8(2):191-197. [CrossRef] [Medline]
    21. Martella D, Aldunate N, Fuentes LJ, Sánchez-Pérez N. Arousal and executive alterations in attention deficit hyperactivity disorder (ADHD). Front Psychol 2020;11:1991 [FREE Full text] [CrossRef] [Medline]
    22. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5®). Washington, DC: American Psychiatric Association Publishing; 2013.
    23. Clarke AR, Barry RJ, McCarthy R, Selikowitz M. Children with attention-deficit/hyperactivity disorder and comorbid oppositional defiant disorder: An EEG analysis. Psychiatry Res 2002 Aug;111(2-3):181-190. [CrossRef]
    24. Posner MI. Imaging attention networks. Neuroimage 2012 Jun;61(2):450-456 [FREE Full text] [CrossRef] [Medline]
    25. Menon V. Large-scale brain networks and psychopathology: A unifying triple network model. Trends Cogn Sci 2011 Oct;15(10):483-506. [CrossRef] [Medline]
    26. Kuperman S, Johnson B, Arndt S, Lindgren S, Wolraich M. Quantitative EEG differences in a nonclinical sample of children with ADHD and undifferentiated ADD. J Am Acad Child Adolesc Psychiatry 1996 Aug;35(8):1009-1017. [CrossRef] [Medline]
    27. Clarke AR, Barry RJ, McCarthy R, Selikowitz M. Age and sex effects in the EEG: Differences in two subtypes of attention-deficit/hyperactivity disorder. Clin Neurophysiol 2001 May;112(5):815-826. [CrossRef]
    28. Markovska-Simoska S, Pop-Jordanova N. Quantitative EEG in children and adults with attention deficit hyperactivity disorder: Comparison of absolute and relative power spectra and theta/beta ratio. Clin EEG Neurosci 2017 Jan;48(1):20-32. [CrossRef] [Medline]
    29. Monastra VJ. Electroencephalographic biofeedback (neurotherapy) as a treatment for attention deficit hyperactivity disorder: Rationale and empirical foundation. Child Adolesc Psychiatr Clin N Am 2005 Jan;14(1):55-82, vi. [CrossRef] [Medline]
    30. Arns M, Heinrich H, Strehl U. Evaluation of neurofeedback in ADHD: The long and winding road. Biol Psychol 2014 Jan;95:108-115. [CrossRef] [Medline]
    31. Snyder SM, Hall JR. A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. J Clin Neurophysiol 2006 Oct;23(5):440-455. [CrossRef] [Medline]
    32. Snyder SM, Quintana H, Sexson SB, Knott P, Haque A, Reynolds DA. Blinded, multi-center validation of EEG and rating scales in identifying ADHD within a clinical sample. Psychiatry Res 2008 Jun 30;159(3):346-358. [CrossRef] [Medline]
    33. Arns M, Conners CK, Kraemer HC. A decade of EEG theta/beta ratio research in ADHD: A meta-analysis. J Atten Disord 2013 Jul;17(5):374-383. [CrossRef] [Medline]
    34. Loo SK, Makeig S. Clinical utility of EEG in attention-deficit/hyperactivity disorder: A research update. Neurotherapeutics 2012 Jul;9(3):569-587 [FREE Full text] [CrossRef] [Medline]
    35. Liechti MD, Valko L, Müller UC, Döhnert M, Drechsler R, Steinhausen H, et al. Diagnostic value of resting electroencephalogram in attention-deficit/hyperactivity disorder across the lifespan. Brain Topogr 2013 Jan;26(1):135-151. [CrossRef] [Medline]
    36. Barry RJ, Clarke AR, Johnstone SJ. A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clin Neurophysiol 2003 Feb;114(2):171-183. [CrossRef]
    37. Adamou M, Fullen T, Jones SL. EEG for diagnosis of adult ADHD: A systematic review with narrative analysis. Front Psychiatry 2020;11:871 [FREE Full text] [CrossRef] [Medline]
    38. Loo SK, Hale TS, Hanada G, Macion J, Shrestha A, McGough JJ, et al. Familial clustering and DRD4 effects on electroencephalogram measures in multiplex families with attention deficit/hyperactivity disorder. J Am Acad Child Adolesc Psychiatry 2010 Apr;49(4):368-377. [CrossRef]
    39. Loo SK, Hale TS, Macion J, Hanada G, McGough JJ, McCracken JT, et al. Cortical activity patterns in ADHD during arousal, activation and sustained attention. Neuropsychologia 2009 Aug;47(10):2114-2119 [FREE Full text] [CrossRef] [Medline]
    40. Ponomarev VA, Mueller A, Candrian G, Grin-Yatsenko VA, Kropotov JD. Group independent component analysis (gICA) and current source density (CSD) in the study of EEG in ADHD adults. Clin Neurophysiol 2014 Jan;125(1):83-97. [CrossRef] [Medline]
    41. Woltering S, Jung J, Liu Z, Tannock R. Resting state EEG oscillatory power differences in ADHD college students and their peers. Behav Brain Funct 2012 Dec 18;8:60 [FREE Full text] [CrossRef] [Medline]
    42. Deiber M, Hasler R, Colin J, Dayer A, Aubry J, Baggio S, et al. Linking alpha oscillations, attention and inhibitory control in adult ADHD with EEG neurofeedback. Neuroimage Clin 2020;25:102145 [FREE Full text] [CrossRef] [Medline]
    43. Missonnier P, Hasler R, Perroud N, Herrmann F, Millet P, Richiardi J, et al. EEG anomalies in adult ADHD subjects performing a working memory task. Neuroscience 2013 Jun 25;241:135-146. [CrossRef] [Medline]
    44. Kiiski H, Bennett M, Rueda-Delgado LM, Farina FR, Knight R, Boyle R, et al. EEG spectral power, but not theta/beta ratio, is a neuromarker for adult ADHD. Eur J Neurosci 2020 May;51(10):2095-2109. [CrossRef] [Medline]
    45. Kaur S, Singh S, Arun P, Kaur D. Analysis of resting state EEG signals of adults with attention-deficit hyperactivity disorder. In: Chaki R, Cortesia A, Saeed K, Chaki N, editors. Advanced Computing and Systems for Security. Advances in Intelligent Systems and Computing, vol 897. In: Springer, Singapore; 2019:61-72.
    46. Neurofeedback Collaborative Group. Double-blind placebo-controlled randomized clinical trial of neurofeedback for attention-deficit/hyperactivity disorder with 13-month follow-up. J Am Acad Child Adolesc Psychiatry 2021 Jul;60(7):841-855. [CrossRef] [Medline]
    47. Vernon D, Egner T, Cooper N, Compton T, Neilands C, Sheri A, et al. The effect of training distinct neurofeedback protocols on aspects of cognitive performance. Int J Psychophysiol 2003 Jan;47(1):75-85. [CrossRef]
    48. Kropotov JD. Functional Neuromarkers for Psychiatry: Applications for Diagnosis and Treatment. London, UK: Academic Press; 2016.
    49. Marzbani H, Marateb HR, Mansourian M. Neurofeedback: A comprehensive review on system design, methodology and clinical applications. Basic Clin Neurosci 2016 Apr;7(2):143-158 [FREE Full text] [CrossRef] [Medline]
    50. Goldberg DP, Gater R, Sartorius N, Ustun TB, Piccinelli M, Gureje O, et al. The validity of two versions of the GHQ in the WHO study of mental illness in general health care. Psychol Med 1997 Jan;27(1):191-197. [CrossRef] [Medline]
    51. DuPaul GJ, Power TJ, Anastopoulos AD, Reid R. ADHD Rating Scale—IV: Checklists, Norms, and Clinical Interpretation. New York, NY: The Guilford Press; 1998.
    52. Ustun B, Adler LA, Rudin C, Faraone SV, Spencer TJ, Berglund P, et al. The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5. JAMA Psychiatry 2017 May 01;74(5):520-527 [FREE Full text] [CrossRef] [Medline]
    53. Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: The GAD-7. Arch Intern Med 2006 May 22;166(10):1092-1097. [CrossRef] [Medline]
    54. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med 2001 Sep;16(9):606-613 [FREE Full text] [CrossRef] [Medline]
    55. Goldberg DPW, Paul DPM. A User’s Guide to the General Health Questionnaire. Windsor, UK: NFER-NELSON; 1988.
    56. Mossman SA, Luft MJ, Schroeder HK, Varney ST, Fleck DE, Barzman DH, et al. The Generalized Anxiety Disorder 7-item scale in adolescents with generalized anxiety disorder: Signal detection and validation. Ann Clin Psychiatry 2017 Nov;29(4):227-234A [FREE Full text] [Medline]
    57. Bardhoshi G, Cobb N, Erford BT. Determining evidence-based outcomes in school-aged youth: Free-access instruments for school counselor use. Prof Sch Couns 2019 Mar 07;22(1b):1-10 [FREE Full text] [CrossRef]
    58. Richardson LP, McCauley E, Grossman DC, McCarty CA, Richards J, Russo JE, et al. Evaluation of the Patient Health Questionnaire-9 item for detecting major depression among adolescents. Pediatrics 2010 Dec;126(6):1117-1123 [FREE Full text] [CrossRef] [Medline]
    59. Riccio CA, Cohen MJ, Hynd GW, Keith RW. Validity of the Auditory Continuous Performance Test in differentiating central processing auditory disorders with and without ADHD. J Learn Disabil 1996 Sep;29(5):561-566. [CrossRef] [Medline]
    60. Greenberg LM, Waldman ID. Developmental normative data on the test of variables of attention (T.O.V.A.). J Child Psychol Psychiatry 1993 Sep;34(6):1019-1030. [CrossRef] [Medline]
    61. Cassuto H, Ben-Simon A, Berger I. Using environmental distractors in the diagnosis of ADHD. Front Hum Neurosci 2013;7:805 [FREE Full text] [CrossRef] [Medline]
    62. Conners CK. Conners, 3rd Edition (Conners 3). Los Angeles, CA: Western Psychological Services; 2008.   URL: https://www.wpspublish.com/conners-3-conners-third-edition [accessed 2022-06-30]
    63. Edwards MC, Gardner ES, Chelonis JJ, Schulz EG, Flake RA, Diaz PF. Estimates of the validity and utility of the Conners' Continuous Performance Test in the assessment of inattentive and/or hyperactive-impulsive behaviors in children. J Abnorm Child Psychol 2007 Jun;35(3):393-404. [CrossRef] [Medline]
    64. Shaked D, Faulkner LMD, Tolle K, Wendell CR, Waldstein SR, Spencer RJ. Reliability and validity of the Conners' Continuous Performance Test. Appl Neuropsychol Adult 2020;27(5):478-487. [CrossRef] [Medline]
    65. Kounali D, Button KS, Lewis G, Gilbody S, Kessler D, Araya R, et al. How much change is enough? Evidence from a longitudinal study on depression in UK primary care. Psychol Med 2020 Nov 03:1-8. [CrossRef]
    66. McGuire JF, Geller DA, Murphy TK, Small BJ, Unger A, Wilhelm S, et al. Defining treatment outcomes in pediatric obsessive-compulsive disorder using a self-report scale. Behav Ther 2019 Mar;50(2):314-324 [FREE Full text] [CrossRef] [Medline]
    67. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol 2018 Dec 05;57(1):289-300. [CrossRef]
    68. Brocki KC, Tillman CM, Bohlin G. CPT performance, motor activity, and continuous relations to ADHD symptom domains: A developmental study. Eur J Dev Psychol 2010 Mar;7(2):178-197. [CrossRef]
    69. Jacobson NS, Truax P. Clinical significance: A statistical approach to defining meaningful change in psychotherapy research. J Consult Clin Psychol 1991;59(1):12-19. [CrossRef]
    70. Parsons TD, Notebaert AJ, Shields EW, Guskiewicz KM. Application of reliable change indices to computerized neuropsychological measures of concussion. Int J Neurosci 2009;119(4):492-507. [CrossRef] [Medline]
    71. Perreau-Linck E, Lessard N, Lévesque J, Beauregard M. Effects of neurofeedback training on inhibitory capacities in ADHD children: A single-blind, randomized, placebo-controlled study. J Neurother 2010 Aug 16;14(3):229-242. [CrossRef]
    72. Rojas GM, Alvarez C, Montoya CE, de la Iglesia-Vayá M, Cisternas JE, Gálvez M. Study of resting-state functional connectivity networks using EEG electrodes position as seed. Front Neurosci 2018;12:235 [FREE Full text] [CrossRef] [Medline]
    73. Firth J, Torous J, Nicholas J, Carney R, Pratap A, Rosenbaum S, et al. The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry 2017 Oct;16(3):287-298 [FREE Full text] [CrossRef] [Medline]
    74. Firth J, Torous J, Nicholas J, Carney R, Rosenbaum S, Sarris J. Can smartphone mental health interventions reduce symptoms of anxiety? A meta-analysis of randomized controlled trials. J Affect Disord 2017 Aug 15;218:15-22 [FREE Full text] [CrossRef] [Medline]
    75. Linardon J, Cuijpers P, Carlbring P, Messer M, Fuller-Tyszkiewicz M. The efficacy of app-supported smartphone interventions for mental health problems: A meta-analysis of randomized controlled trials. World Psychiatry 2019 Oct;18(3):325-336 [FREE Full text] [CrossRef] [Medline]
    76. Wu A, Scult MA, Barnes ED, Betancourt JA, Falk A, Gunning FM. Smartphone apps for depression and anxiety: A systematic review and meta-analysis of techniques to increase engagement. NPJ Digit Med 2021 Feb 11;4(1):20 [FREE Full text] [CrossRef] [Medline]
    77. Hull TD, Mahan K. A study of asynchronous mobile-enabled SMS text psychotherapy. Telemed J E Health 2017 Mar;23(3):240-247. [CrossRef] [Medline]
    78. Venkatesan A, Rahimi L, Kaur M, Mosunic C. Digital cognitive behavior therapy intervention for depression and anxiety: Retrospective study. JMIR Ment Health 2020 Aug 26;7(8):e21304 [FREE Full text] [CrossRef] [Medline]
    79. Ryoo M, Son C. Effects of neurofeedback training on EEG, continuous performance task (CPT), and ADHD symptoms in ADHD-prone college students. J Korean Acad Nurs 2015 Dec;45(6):928-938. [CrossRef] [Medline]
    80. Rossiter T. The effectiveness of neurofeedback and stimulant drugs in treating AD/HD: Part II. Replication. Appl Psychophysiol Biofeedback 2004 Dec;29(4):233-243. [CrossRef] [Medline]
    81. Kitsune GL, Cheung CHM, Brandeis D, Banaschewski T, Asherson P, McLoughlin G, et al. A matter of time: The influence of recording context on EEG spectral power in adolescents and young adults with ADHD. Brain Topogr 2015 Jul;28(4):580-590 [FREE Full text] [CrossRef] [Medline]
    82. Bresnahan SM, Barry RJ. Specificity of quantitative EEG analysis in adults with attention deficit hyperactivity disorder. Psychiatry Res 2002 Oct;112(2):133-144. [CrossRef]
    83. Rowe DL, Robinson PA, Lazzaro IL, Powles RC, Gordon E, Williams LM. Biophysical modeling of tonic cortical electrical activity in attention deficit hyperactivity disorder. Int J Neurosci 2005 Sep;115(9):1273-1305. [CrossRef] [Medline]
    84. Schutter DJ, Leitner C, Kenemans JL, van Honk J. Electrophysiological correlates of cortico-subcortical interaction: A cross-frequency spectral EEG analysis. Clin Neurophysiol 2006 Feb;117(2):381-387. [CrossRef] [Medline]
    85. Koehler S, Lauer P, Schreppel T, Jacob C, Heine M, Boreatti-Hümmer A, et al. Increased EEG power density in alpha and theta bands in adult ADHD patients. J Neural Transm (Vienna) 2009 Jan;116(1):97-104. [CrossRef] [Medline]
    86. Poil S, Bollmann S, Ghisleni C, O'Gorman RL, Klaver P, Ball J, et al. Age dependent electroencephalographic changes in attention-deficit/hyperactivity disorder (ADHD). Clin Neurophysiol 2014 Aug;125(8):1626-1638. [CrossRef] [Medline]
    87. Hermens DF, Williams LM, Lazzaro I, Whitmont S, Melkonian D, Gordon E. Sex differences in adult ADHD: A double dissociation in brain activity and autonomic arousal. Biol Psychol 2004 Jul;66(3):221-233. [CrossRef] [Medline]
    88. van Dongen-Boomsma M, Lansbergen MM, Bekker EM, Kooij JJS, van der Molen M, Kenemans JL, et al. Relation between resting EEG to cognitive performance and clinical symptoms in adults with attention-deficit/hyperactivity disorder. Neurosci Lett 2010 Jan 18;469(1):102-106. [CrossRef] [Medline]
    89. Saad JF, Kohn MR, Clarke S, Lagopoulos J, Hermens DF. Is the theta/beta EEG marker for ADHD inherently flawed? J Atten Disord 2018 Jul;22(9):815-826. [CrossRef] [Medline]
    90. Bresnahan SM, Barry RJ, Clarke AR, Johnstone SJ. Quantitative EEG analysis in dexamphetamine-responsive adults with attention-deficit/hyperactivity disorder. Psychiatry Res 2006 Feb 28;141(2):151-159. [CrossRef] [Medline]
    91. Cooper RE, Skirrow C, Tye C, McLoughlin G, Rijsdijk F, Banaschweski T, et al. The effect of methylphenidate on very low frequency electroencephalography oscillations in adult ADHD. Brain Cogn 2014 Apr;86:82-89. [CrossRef] [Medline]
    92. Berger I, Dor T, Nevo Y, Goldzweig G. Attitudes toward attention-deficit hyperactivity disorder (ADHD) treatment: Parents' and children's perspectives. J Child Neurol 2008 Sep;23(9):1036-1042. [CrossRef] [Medline]
    93. McHugh RK, Whitton SW, Peckham AD, Welge JA, Otto MW. Patient preference for psychological vs pharmacologic treatment of psychiatric disorders. J Clin Psychiatry 2013 Jun 15;74(06):595-602. [CrossRef]
    94. Razoki B. Neurofeedback versus psychostimulants in the treatment of children and adolescents with attention-deficit/hyperactivity disorder: A systematic review. Neuropsychiatr Dis Treat 2018 Oct;Volume 14:2905-2913. [CrossRef]
    95. Duffy FH. The state of EEG biofeedback therapy (EEG operant conditioning) in 2000: An editor's opinion. Clin Electroencephalogr 2000 Jan;31(1):V-VII. [Medline]

    ‎
    ADHD: attention-deficit/hyperactivity disorder
    ADHD-RS-IV: ADHD Rating Scale IV
    AF: anterior frontal
    ASRS: Adult ADHD Self-Report Scale
    BH: Benjamini-Hochberg
    C: central
    CPT: continuous performance task
    DAR: delta/alpha ratio
    DSM-5: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition
    EEG: electroencephalography
    F: frontal
    GAD-7: 7-item Generalized Anxiety Disorder scale
    GHQ-12: 12-item General Health Questionnaire
    IRB: Institutional Review Board
    NFT: neurofeedback training
    O: occipital
    PBH: Benjamini-Hochberg–adjusted P value
    PHQ-9: 9-item Patient Health Questionnaire
    RCI: reliable change index
    RT: response time
    SDRT: SD of response time, response time variability
    SMR: sensorimotor rhythm
    TAR: theta/alpha ratio
    TBR: theta/beta ratio
    TP: temporal pole
    z: zero

    Edited by A Mavragani; submitted 13.12.21; peer-reviewed by DLG Rodrigues; comments to author 20.05.22; revised version received 31.05.22; accepted 09.06.22; published 08.07.22

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    ©Jocelyne C Whitehead, Ron Neeman, Glen M Doniger. Originally published in JMIR Formative Research (https://formative.jmir.org), 08.07.2022.

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