Validity of Visual and Auditory Attention Tests for Detecting ADHD

Abstract

Objective: This investigation examines the discriminative validity of visual and auditory attention tests for differentiating patients with ADHD from healthy control participants. Method: A total of 107 ADHD patients and 58 healthy control participants were recruited. Visual and auditory attention profiles were obtained using the Conners’ Continuous Performance Test 3rd Edition (CPT3) and Conners’ Continuous Auditory Test of Attention (CATA), respectively. Results: We found that ADHD patients underperformed healthy controls on all CPT3 and CATA indexes, except Response Style and Hit Reaction Time. The CPT3, CATA, and CPT3 plus CATA all significantly differentiate ADHD patients and controls. CPT3 plus CATA had a greater sensitivity (82.6%), specificity (76%), positive predictive value (88.8%), negative predictive value (65.5%), and overall correct classification rate (80.6%) than CPT3 or CATA alone. Conclusion: Neuropsychological tests CPT3 and CATA provide objective information about cases of ADHD and should be used routinely for clinical assessment.

Keywords

ADHD neuropsychological test visual attention auditory attention clinical symptoms

Introduction

ADHD is a psychiatric disorder that is commonly exhibited by children and adolescents; it is characterized by inattention, hyperactivity, and impulsivity (Thapar & Cooper, 2016). This disorder affects 3% to 10% of school-age children globally (Polanczyk, Willcutt, Salum, Kieling, & Rohde, 2014). The early detection and proper diagnosis of ADHD are crucial for providing clinicians an opportunity to intervene in, and mitigate patients’ functional impairments throughout their lives (Geissler & Lesch, 2011). The diagnosis of ADHD depends on gathering information about behavioral symptoms from patients as well as their parents, teachers, or clinical observers. However, interviews and data collection are time-consuming and behavioral measures are vulnerable to rater bias and reporting bias (Polanczyk & Moffitt, 2014; Solanto & Alvir, 2009). Therefore, efficient and reliable objective measurements, such as obtained in neuropsychological tests, may augment and streamline current practice, shortening assessment times and increasing diagnostic accuracy (Hall et al., 2016). Neuropsychological research has shed light on a wide range of neuropsychological deficits, including those of executive function and inhibitory control, in ADHD patients (Mahone & Denckla, 2017).

Central attentional resources are common to all sensory modalities (Salo, Rinne, Salonen, & Alho, 2013). Children with ADHD have been demonstrated to have impaired sustained attention and slow visual processing (McAvinue et al., 2015). A continuous performance test (CPT) is a computer-based neuropsychological test that is frequently used to measure an individual’s attention and impulsivity during a sustained task; it can be used in a clinical inquiry as part of the diagnostic procedure (Berger, Slobodin, & Cassuto, 2017; Hall et al., 2016). Several studies have suggested that combining CPTs with an objective measure of activity may be particularly useful for clinical purposes (Corkum & Siegel, 1993; Koelega, 1995; Lichtenstein, Flaro, Baldwin, Rai, & Erdodi, 2019; McGee, Clark, & Symons, 2000; Miranda et al., 2012). In addition to visual attention impairments, patients with ADHD exhibit deficits in auditory vigilance tests and become less careful when interference is introduced (Fabio, Castriciano, & Rondanini, 2015; Stavrinos, Iliadou, Edwards, Sirimanna, & Bamiou, 2018). Gomes et al. (2012) analyzed electrophysiological markers (Nd, P3b, and Ta) during an auditory selective attention task in children with ADHD and found ADHD children exhibited worse target detection performance than that of their typically developing counterparts.

Owing to the difference between primary auditory and visual perceptual systems, attention may be modality specific (Khaleghi, Zarafshan, & Mohammadi, 2019; Waddington et al., 2018). Some investigations have simultaneously obtained visual and auditory attention profiles for patients with ADHD (Moreno-Garcia, Delgado-Pardo, & Roldan-Blasco, 2015; Schmidt, Simoes, & Novais Carvalho, 2019; Simoes, Carvalho, & Schmidt, 2018). Lin et al. (2017) used the Test of Variables of Attention (TOVA) as a primary assessment tool and found that the impairment of visual attention is more serious than that of auditory attention in children with ADHD. High variability of attention is an important index in diagnosing and intervening in ADHD when both auditory and visual modalities are considered. J. Kim et al. (2015) demonstrated that the integrated visual and auditory CPT (IVA+CPT), as well as quantitative electroencephalography (QEEG), significantly distinguished between ADHD and control groups. The correct classification rate of ADHD diagnosis using commission error and omission error of IVA+CPT were 82.1% and 78.6%, respectively.

Conners’ CPT 3rd Edition (CPT3) is an extensively used tool for assessing deficits of visual attention and for assessing attention and inhibitory control in both children and adults (Conners, 2004). Perugini, Harvey, Lovejoy, Sandstrom, and Webb (2000) revealed a sensitivity and specificity of 75.6% and 63% for CPT3 in distinguishing between ADHD patients and controls. Conners’ Continuous Auditory Test of Attention (CATA) assesses auditory processing and attention-related problems (Rassovsky & Alfassi, 2018). Current diagnosis guidelines do not view the inclusion of neuropsychological tasks as an essential component of ADHD evaluations (Pelham, Fabiano, & Massetti, 2005; Pliszka & AACAP Work Group on Quality Issues, 2007; Subcommittee on Attention-Deficit/Hyperactivity Disorder et al., 2011). However, none of previous studies had compared the validity of CPT3 with that of CATA for detecting ADHD. We propose that both visual and auditory tests can together distinguish effectively between patients with ADHD and controls. The aim of this investigation is to compare visual and auditory attention profiles through CPT3 and CATA tests between children with ADHD and healthy controls, and to determine whether combining tests of visual and auditory attention can achieve greater discriminative validity than Conners’ CPT3 or CATA alone for differentiating patients with ADHD from healthy control participants.

The aim of this investigation is to compare whether Conners’ CPT3 or CATA test alone or their combination may achieve greater discriminative validity to differentiate patients with ADHD from healthy control. Our result may indicate whether visual or auditory attention profiles or the combination of both attention profiles may differentiate patients with ADHD from healthy control participants better.

Method

Study Participants

The Institutional Review Board (IRB) at Chang Gung Memorial Hospital in Taiwan approved the research protocol. Written informed consent was obtained from the parents or guardians of all participating children. This study involved eligible patients with ADHD who were being treated in the outpatient Department of Child Psychiatry at Chang Gung Children’s Hospital in Taiwan along with healthy control children.

The inclusion criteria for ADHD patients were as follows: (a) a clinical diagnosis of ADHD by a senior child psychiatrist based on the criteria in the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; DSM-IV-TR; American Psychiatric Association [APA], 2000) following structured interviews based on the Chinese version of the Schedule for Affective Disorders and Schizophrenia for School-Age Children, epidemiologic version (K-SADS-E; Kaufman et al., 1997); (b) aged between 6 and 16 years; (c) a Han Chinese ethnic background; and (d) never taken any medications to treat ADHD. Patients with major physical illnesses (such as genetic, metabolic, or infectious conditions) or a history of comorbid major neuropsychiatric diseases (such as intellectual disabilities, autism spectrum disorder, bipolar disorders, major depressive disorders, psychotic disorders, substance use disorders, epilepsy, or severe head trauma) were excluded.

The healthy control participants were children without ADHD, who were ethnically Han Chinese, between the ages of 6 and 16 years, and from the same catchment area. All healthy controls were assessed by K-SADS-E. Based on K-SADS-E, children without any known major physical illnesses or any of the aforementioned major neuropsychiatric diseases were enrolled in this study.

Clinical Measurements

A senior psychiatrist used the K-SADS-E diagnostic tool in interviewing all participants of both the ADHD patient group and the control group. Both the ADHD patients and controls were assessed using the Conners’ Continuous Performance Test 3rd Edition (Conners CPT3), Conners’ Continuous Auditory Test of Attention™ (Conners CATA™), the Wechsler Intelligence Scale for Children–Fourth Edition (WISC-IV), the Swanson, Nolan, and Pelham Version IV Scale (SNAP-IV) parent form and SNAP-IV teacher form. An experienced child psychologist conducted neuropsychological tests using the WISC-IV and Conners’ CPT3 and CATA. The SNAP-IV parent form and SNAP-IV teacher form were completed by the patients’ parents and the main classroom teacher, respectively.

Conners CPT3 is administered in a quiet testing room. The test takes approximately 15 to 20 min, and participants are asked to click a mouse button in response to target stimuli that appear on a screen as rapidly as possible whenever any letter except X appears. When X appears, the examinee is instructed to refrain from responding to the nontarget stimulus. The interstimuli intervals (ISIs) are 1 s, 2 s, 4 s with a display time of 250 ms. The test consists of six blocks, comprising three subblocks, each of 20 trials. Several raw scores are ultimately derived. Generally, higher raw scores indicate poorer performance (Tu et al., 2018). The test involves the following parameters; Response Style (C), Detectability (d’), Omissions, Commissions, Perseverations, Hit Reaction Time (HRT), HRT Standard Deviation and Variability.

Conners CATA™ assesses auditory processing and attention-related problems. During its 14-min administration, a 200-trial protocol (divided into four blocks) is used to assess the respondents’ performance in inattentiveness, impulsivity, and sustain attention, as well as to provide information about the respondents’ auditory laterality (Rassovsky & Alfassi, 2018). Within each block, 80% of the trials are warned trials in which a low tone (warning) is followed by a high tone (target). The remaining 20% of the trials are unwarned trials in which the high tone is played without warning. Respondents are asked to respond to high tones on warned trials, but ignore those on unwarned trials. Respondents are also asked to listen for the target sound using the same ear in which they heard the warning sound. On 75% warned trials, the warning and the target sounds played sequentially in the same ear (nonswitch trials). On 25% warned trials, however, the warning and the target sounds are played in opposite ears (switch trials). On these switch trials, the warning sound is in an invalid cue to the location of the target sound, and the respondent is required to switch attention from one ear to another. Overall, an equal number of high tones are played to each ear. The measures used in the analyses are Detectability (d’), Omissions, Commissions, Perseveration, Hit Reaction Time (HRT), HRT Standard Deviation (SD), and HRT Block Change. Conners’ CATA can be used to evaluate attention disorders and neurological functioning (Y. Kim et al., 2009).

The K-SADS-E is a semistructured diagnostic interview for assessing current and previous episodes of psychopathology in children and adolescents according to Diagnostic and Statistical Manual of Mental Disorders (3rd ed., rev.; DSM-III-R; APA, 1987) and Diagnostic and Statistical Manual of Mental Disorders (4th ed.; DSM-IV; APA, 1994) criteria (Kaufman et al., 1997). The K-SADS-E is administered by interviewing the child of interest and his or her parent(s) to provide summary ratings that are derived from all sources of observers’ information. The validity and reliability of the Chinese version of K-SADS-E in Taiwan has been established (S. F. Gau & Soong, 1999).

WISC-IV–Chinese Version is an individually administered and norm-referenced instrument that is used to measure the intelligence of children aged from 6 to 16 (Yang et al., 2013). The WISC-IV test comprises 10 core and five supplemental subtests. The core subtests provide four factor indexes: the Verbal Comprehension Index (VCI), the Perceptual Reasoning Index (PRI), the Working Memory Index (WMI), and the Processing Speed Index (PSI). The Full-Scale Intelligence Quotient (FSIQ) is also obtained from the 10 core subtests. The factor indexes and FSIQ each have a population mean of 100 and a standard deviation of 15 (Baron, 2005).

The SNAP-IV is associated with a 26-item questionnaire and is used to evaluate ADHD symptoms and severity. It is completed by parents or teachers (Bussing et al., 2008). The 26 items comprise 18 that pertain ADHD symptoms (nine for inattention and nine for hyperactivity and impulsivity) and eight that concern oppositional defiant disorder symptoms, as defined in the DSM-IV-TR. Each item is scored on a 3-point Likert-type scale. The Chinese versions of the SNAP-IV parent form (S. S. Gau et al., 2008) and the SNAP-IV teacher form (S. S. Gau et al., 2009) have reportedly have satisfactory reliability and concurrent validity.

Statistical Analysis

Data were analyzed using the statistical software package SPSS, version 16.0 (SPSS Inc., Chicago, IL, USA) and the MedCalc software Version 15.11.4. Variables were presented as either mean (standard deviation) or frequency. Two-tailed p values of <.05 were regarded as indicating statistical significance.

The chi-square test or Fisher’s exact test was used to compare the gender distributions of the ADHD patients and the controls. An independent t test was carried out to identify any difference between clinical and neuropsychological assessments. Logistic regression yielded composite probability scores of CPT3, CATA, and CPT3 plus CATA. The T-scores of each index of CPT3, CATA, and CPT3 plus CATA were included in the regression model. Three probability scores, which represent composite scores of CPT3, CATA, and CPT3 plus CATA were generated from the models. We further analyzed the receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to evaluate the specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV) of each aforementioned probability score that was obtained by logistic regression. Our study aim was to identify which test had greater discriminative validity, which is represented by greater AUC. The optimal diagnostic point of the signature was evaluated at the cutoff values at a probability score of 0.5. Pearson correlation was used to determine the correlation between the probability score and ADHD symptoms in all patients. Two-sample proportional test was applied to examine the difference in specificity, sensitivity, PPV, NPV, and overall correct classification rate between the CPT+CATA versus either test alone.

Sensitivity analyses were performed to evaluate the robustness of our results. First, all participants were divided into a younger group (<10 years) and an older group (≥10 years), and into a lower IQ group (<100) and a higher IQ group (≥100). Then, ROC analysis was conducted to determine whether the probability scores that were generated by logistic regression effectively differentiated patients from controls in each age-, sex- and FSIQ-stratified group.

Results

Demographic Data

Table 1 characterizes the ADHD patients and the healthy controls. A total of 107 ADHD patients (mean age 9.6 years, 78.5% males) and 58 healthy control participants (mean age 9.9 years, 60.3% males) were recruited. ADHD patients were more likely than the control participants to be male (p = .024) and they had a lower intelligence quotient (p < .001), as measured using the WISC-IV, higher inattention scores (p < .001), and higher hyperactivity/impulsivity scores (p < .001), as rated by parents and teachers.

Table 1.

Characteristics of Patients With ADHD and Healthy Controls.

Characteristics	ADHD (n = 107)	Controls (n = 58)	Statistic	p value
Sex			6.169	.013*
Male	84 (78.5)	35 (60.3)
Female	23 (21.5)	23 (39.7)
Age (years)	9.6 ± 2.1	9.9 ± 2.0	1.046	.297
Comorbidity
Oppositional defiant disorder
WISC-IV
FSIQ	97.1 ± 10.6	108.2 ± 12.9	5.936	<.001*
VCI	99.0 ± 9.6	105.1 ± 10.9	3.705	<.001*
PRI	98.3 ± 12.8	110.9 ± 15.6	5.268	<.001*
WMI	99.0 ± 13.2	107.8 ±11.5	4.281	<.001*
PSI	93.9 ± 9.8	101.6 ± 10.4	4.740	<.001*
Clinical measures
SNAP-IV parent form (I)	16.2 ± 5.3	5.1 ± 5.6	12.505	<.001*
SNAP-IV parent form (H)	13.4 ± 6.2	3.4 ± 4.2	12.092	<.001*
SNAP-IV teacher form (I)	14.6 ± 5.9	4.3 ± 4.8	11.919	<.001*
SNAP-IV teacher form (H)	11.1 ± 6.9	2.7 ± 3.6	9.911	<.001*
CPT3
Response Style (C)	52.1 ± 9.6	50.3 ± 11.7	1.079	.282
Detectability (d’)	51.1 ± 9.0	47.2 ± 11.2	2.428	.016*
Omissions	58.0 ± 18.0	51.3 ± 13.6	2.675	.008*
Commissions	49.1 ± 10.0	44.9 ± 11.1	2.487	.014*
Perseverations	58.6 ± 17.3	47.5 ± 4.9	6.238	<.001*
HRT	55.3 ± 13.7	55.2 ± 12.0	0.067	.947
HRT Standard Deviation	57.2 ± 12.1	51.8 ± 9.6	2.907	.004*
Variability	56.4 ± 10.8	52.4 ± 9.4	2.351	.020*
CATA
Detectability (d’)	54.7 ± 7.4	49.2 ± 8.7	4.217	<.001*
Omissions	51.8 ± 8.4	48.2 ± 5.8	2.875	.005
Commissions	53.8 ± 9.2	49.8 ± 7.0	2.939	.004*
Perseveration	53.3 ± 7.4	49.1 ± 6.8	3.534	.001*
Hit Reaction Time (HRT)	53.9 ± 8.0	56.1 ± 9.0	1.634	.104
HRT Standard Deviation	55.2 ± 8.0	50.4 ± 9.7	3.410	.001*
HRT Block Change	52.1 ± 10.0	51.8 ± 11.1	0.176	.861

Note. Data are expressed as n (%) or M ± SD. WISC-IV = Wechsler Intelligence Scale for Children–Fourth Edition; FSIQ = Full-Scale Intelligence Quotient; VCI = Verbal Comprehension Index; PRI = Processing Speed Index; WMI = Working Memory Index; PSI = Processing Speed Index; SNAP-IV = Swanson, Nolan, and Pelham Version IV Scale; CPT = Continuous Performance Test; HRT = Hit Reaction Time; CATA = Continuous Auditory Test of Attention; I = inattention scores; H = hyperactivity/impulsivity scores.

p < .05.

ADHD patients underperformed the control participants with respect to Omission (p = .008), Commission (p = .014), Hit RT SD (p = .004), Variability (p = .020), Detectability (p = .016), and Perseveration (p < .001) on the CPT3, and Detectability (p < .001), Omission (p = .005), Commission (p = .004), Perseveration (p = .001), and Hit RT SD (p = .001) on the CATA.

Predictive Validity of the CPT3 and CATA

Logistic regression was used to obtain composite scores of the results of the CPT3, CATA, and CPT3+CATA. Figure 1 plots the ROC curves that were generated using the predicted probability scores to differentiate ADHD patients from controls. The probability scores that were predicted by CPT3 (AUC = 0.829, p < .001), CATA (AUC = .740, p < .001), and CPT3+CATA (AUC = .907, p < .001) all significantly differentiate ADHD from controls.

Figure 1.

ROC curves obtained using CPT3, CATA, and CPT3 plus CATA for differentiating between ADHD patients and healthy controls.

Figure 2 presents the discriminative validity (sensitivity, specificity, PPV, and NPV) of the composite scores that were obtained from the results CPT3, CATA, and CPT3+CATA. CPT3 plus CATA had a greater sensitivity (82.6%), specificity (76%), PPV (88.8%), NPV (65.5%), and overall correct classification rate (80.6%) than the CPT3 or CATA alone. Using two-sample proportional test, we found that CPT+CATA showed significantly better specificity (z = 2.56, p = .010) and NPV (z = 2.85, p = .004) than CPT alone. Moreover, CPT+CATA showed significantly better NPV (z = 3.46, p < .001) than CATA alone.

Figure 2.

Discriminative validity (sensitivity, specificity, positive predictive value, negative predictive value) of composite scores obtained using CPT3, CATA, and CPT3 plus CATA.

Finally, the probability scores that were yielded by the CPT3, CATA, and CPT3+CATA were observed to be positively correlated with inattention symptoms and hyperactivity/impulsivity symptoms, as rated by parents and teachers (Table 2). Notably, 156 (94.5%) participants had teacher rating SNAP-IV scales available. Compared with the results of the CPT3 (Pearson correlation coefficient [r] ranged from 0.316 to 0.470, p < .001) and CATA (r ranged from 0.332 to 0.405, p < .001), the results of the CPT3+CATA were more strongly related to ADHD symptoms (r ranged from 0.384 to 0.564, p < .001).

Table 2.

Correlation Between Composite Probability Scores of CPT3, CATA, and CPT3 plus CATA and ADHD Behavioral Symptoms Among All Participants.

	CPT	CATA	CPT+CATA
SNAP-IV parent form (I)	0.470***	0.391***	0.564***
SNAP-IV parent form (H)	0.440***	0.405***	0.497***
SNAP-IV teacher form (I)	0.390***	0.351***	0.502***
SNAP-IV teacher form (H)	0.316***	0.332***	0.384***

Note. Data are expressed as Pearson correlation coefficient. CPT = Continuous Performance Test; CATA = Continuous Auditory Test of Attention; SNAP-IV = Swanson, Nolan, and Pelham Version IV Scale.

***

p < .001.

Sensitivity Analyses

As shown in Supplementary Figure 1, the probability scores that were predicted using CPT3, CATA, and CPT3+CATA all significantly differentiate ADHD from controls, irrespective of sex, age, and FSIQ. For sex-stratified analyses, CPT3 (boys: AUC = 0.793, p < .001; girls: AUC = .862, p < .001), CATA (boys: AUC = .694, p = .001; girls: AUC = .767, p = .002), and CPT3+CATA (boys: AUC = .804, p < .001; girls: AUC = 1.000, p < .001) all significantly differentiate ADHD from controls. For age-stratified analyses, CPT3 (age < 10 years: AUC = 0.766, p < .001; age ≥10 years: AUC = 0.923, p < .001), CATA (age < 10 years: AUC = 0.697, p = .001; age ≥ 10 years: AUC = 0.834, p < .001), and CPT3+CATA (age < 10 years: AUC = 0.892, p < .001; age ≥10 years: AUC = 0.942, p < .001) all significantly differentiate ADHD from controls. For FSIQ-stratified analyses, CPT3 (FSIQ < 100: AUC = 0.807, p < .001; FSIQ ≥100: AUC = 0.837, p < .001), CATA (FSIQ < 100: AUC = 0.695, p = .016; FSIQ ≥100: AUC = 0.775, p < .001), and CPT3+CATA (FSIQ < 100: AUC = 0.918, p < .001; FSIQ ≥100: AUC = 0.911, p < .001) all significantly differentiate ADHD from controls.

Discussion

This is the first study which used CPT3 and CATA simultaneously to obtain information of visual attention and auditory attention, and compared their discriminative validities for differentiating ADHD from controls. The main results in this study are that ADHD patients exhibited worse visual and auditory attention than healthy controls. CPT3 and CATA both effectively distinguish patients with ADHD from healthy controls. The discriminative validity of CPT3 and CATA combined for differentiating ADHD patients from controls exceeds that of CPT3 or CATA alone. The discriminative validity is unaffected by age, gender, or intelligence quotient, indicating the robustness of the findings of this study.

Conners’ CPT is a popular research tool that is used to assist in the clinical diagnosis of ADHD. Most relevant works have focused on CPT 2nd version (Conners’ CPT-II; Hall et al., 2016), which is used in the diagnostic assessment of children with ADHD when teacher and parent ratings are inconclusive (Tallberg, Rastam, Wenhov, Eliasson, & Gustafsson, 2019). The present investigation is pioneering in its use of CPT3 and CATA simultaneously to obtain delineating visual and auditory attention profiles for patients with ADHD. ADHD patients underperformed controls with respect to all CPT3 and CATA indexes, except Response Style (C) and HRT. A higher C score indicates a more conservative response style, and a higher HRT indicates slower responses. These two indexes may not strongly capture the poor inhibition control and impulsivity of ADHD patients. However, high Omission, Commission and Perseveration errors, high Hit RT SD indicating a low consistency of response speed, high variability of response speeds across subblocks, and a low ability to discriminate targets from nontargets were consistently observed in the results of CPT3 (visual) and CATA (auditory) for ADHD patients. The auditory and visual attention systems have different developmental trajectories (Dawes & Bishop, 2008). A developmental lag in maturation of the brain in patients with ADHD may be the biological pathophysiology that underpins the consistent deficits in visual and auditory attention (Guy, Rogers, & Cornish, 2013). The finding in this work supports the claim that a quantitative measure may be used in support of ADHD diagnosis (Jarrett, Meter, Youngstrom, Hilton, & Ollendick, 2018).

Either visual attention (CPT3) or auditory attention (CATA) significantly differentiated between ADHD and controls. Alloway et al. (2009) used discriminant function analysis and found that Conners’ CPT correctly classified 41% of ADHD children and 65% of healthy controls. Perugini et al. (2000) reported a sensitivity and specificity of 67% and 73%, respectively, for Conners’ CPT in classifying ADHD boys from healthy boys. The preset investigation revealed a sensitivity and specificity of 75.6% and 63% for CPT3 in distinguishing between ADHD patients and controls. J. Kim et al. (2015) demonstrated that the IVA+CPT and QEEG had a correct classification rate of 78.6% to 82.1% in ADHD diagnosis. The overall classification accuracy of CPT3 and CATA combined for identifying ADHD was 80.6%, which is comparable to that obtained by Kim et al. The CPT3 and CATA are routinely used in clinical setting for the assessment of ADHD; these tools are clinically feasible and widely used.

Children with ADHD exhibit poor sustained attention and slow visual processing (McAvinue et al., 2015). The auditory modality and auditory attention are important to many areas of learning and functioning. Some ADHD patients exhibit prominent visual attention deficits while others exhibit more auditory attention deficits (Fabio et al., 2015; Stavrinos et al., 2018). In our study, 11 ADHD patients showed CPT3 abnormality but no abnormality of CATA performance. In contrast, 15 ADHD patients showed CATA abnormality but no abnormality of CPT3 performance. Etiology of deficits of visual attention and auditory attention might be discrepant. Recent neurobiological data revealed that mechanism of visual attention is associated with network of areas in frontal and parietal cortex (Mueller, Hong, Shepard, & Moore, 2017). Temporal network and frontal network seem to be involved during selective auditory attention (Tzourio et al., 1997). The clinical implication of this investigation is that combining visual attention and auditory attention assessments might be helpful in gaining a comprehensive neuropsychological picture that increases the accuracy of ADHD identification. By preserving auditory or visual attention in these patients, clinicians may psychoeducate patients to use the preserved attention to compensate for their deficits. However, it requires a larger sample to examine whether the correct classification rate of CPT3 and CATA combined for distinguishing between ADHD and healthy controls exceeded that of either CPT3 or CATA alone.

Sensitivity tests were carried out and found that CPT3 plus CATA had greater discriminative validity (AUC) than CPT3 or CATA alone, independently of sex, age, and FSIQ. This result implies that (visual and auditory deficits can serve as useful adjuncts for identifying ADHD in clinical practice, regardless of the characteristics of the ADHD patient. Furthermore, the probability scores that were obtained by CPT3, CATA, and CPT3 plus CATA were positively correlated with inattention symptoms and hyperactivity/impulsivity symptoms, as rated by parents and teachers. The results of CPT3 plus CATA were the most strongly related to ADHD symptoms. Arble, Kuentzel, and Barnett (2014) indicated that general cognitive ability was associated with better performance on attention tasks. Epstein et al. (2003) reported that overall CPT performance in terms of the two signal detection measures, d’ and beta, was strongly related to all ADHD symptoms across symptom domains. Moreover, increased variability in hit response time was associated with most ADHD symptoms (Epstein et al., 2003). However, McGee et al. (2000) reported that higher Conners’ CPT scores were not strongly correlated with clinical manifestations of ADHD. McGee et al. suggested that despite the advantages of the Conners’ CPT, its utility in the differential diagnosis of ADHD is questionable. Nonetheless, the findings in this work reveal that, when overall attention performance is considered, the degree of deficit in neuropsychological function is positively correlated with the severity of behavioral symptoms.

This study has several limitations that must be mentioned. First, the sample was small, and the ADHD patients and controls were not matched with respect to age and gender. In addition, the ADHD study population contained adolescents up to age 16 who have never been medicated. This patient group may be biased toward mild ADHD symptoms, mostly inattentive type, higher intelligence, or fewer oppositional and disruptive behaviors. The ADHD sample enrolled in this study may not be representative for overall children with ADHD. Second, CPT3 and CATA yielded composite orders following the application of the logistic regression model. The parameters of each index may vary across samples. Third, although we used K-SADS-E to exclude comorbidity of major neuropsychiatric diseases (such as intellectual disabilities, autism spectrum disorder, bipolar disorders, major depressive disorders, psychotic disorders, substance use disorders, epilepsy, or severe head trauma), some comorbidities of patients were not analyzed. For example, we did not assess the comorbidity of learning disability, which has been shown to be associated with attention problems (Jarrett et al., 2018). Our study result should be interpreted with caution. Finally, the participants in this study were all Han Chinese and recruited at a single site in Taiwan. An independent validation sample must be carried out to verify whether the findings in this investigation can be generalized to another ADHD group.

In conclusion, ADHD patients exhibit worse visual and auditory attention than healthy controls. Assessments of both visual and auditory attention may assist in the clinical diagnosis of ADHD and increase its accuracy. The findings of this study support the claim that neuropsychological tests that combine CPT3 and CATA may provide objective information about ADHD, and can be routinely used for assessment in clinical settings.

Supplemental Material

Figure_S1 – Supplemental material for Validity of Visual and Auditory Attention Tests for Detecting ADHD

Supplemental material, Figure_S1 for Validity of Visual and Auditory Attention Tests for Detecting ADHD by Liang-Jen Wang, Sheng-Yu Lee, Ching-Shu Tsai, Min-Jing Lee, Miao-Chun Chou, Ho-Chang Kuo and Wen-Jiun Chou in Journal of Attention Disorders

Footnotes

Acknowledgements

The authors thank Professor Wei-Tsun Soong for granting us the use of the Chinese version of the K-SADS and Professor Shur-Fen Gau for granting our use of the Chinese version of the SNAP-IV.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by grant from the Taiwan Ministry of Science and Technology (MOST 104-2314-B-182A-032 and MOST 105-2314-B-182A-054-MY2).

ORCID iD

Liang-Jen Wang

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Liang-Jen Wang is a senior child psychiatrist in Kaohsiung Chang Gung Memorial Hospital and an associated professor in Chang Gung University College of Medicine, Taiwan. His main research fields are biomarkers and neuropsychological function of ADHD.

Sheng-Yu Lee is a senior psychiatrist in Department of Psychiatry, Kaohsiung Veterans General Hospital. Her major research fields are mood disorders and substance use disorders.

Ching-Shu Tsai is currently the chief of child psychiatrist in Kaohsiung Chang Gung Memorial Hospital. Her research interest is neuropsychological function of ADHD.

Min-Jing Lee is a child psychiatrist in Kaohsiung Chang Gung Memorial Hospital. Her research interest is neuropsychological function of ADHD.

Miao-Chun Chou is a senior psychiatrist in Kaohsiung Chang Gung Memorial Hospital. Her research interest is neuropsychological function of ADHD.

Ho-Chang Kuo is a senior pediatrician in Kaohsiung Chang Gung Memorial Hospital and a professor in Chang Gung University College of Medicine. His main research fields is about Kawasaki disorder.

Wen-Jiun Chou is a senior child psychiatrist in Kaohsiung Chang Gung Memorial Hospital and an associated professor in Chang Gung University College of Medicine. His main research fields are biomarkers and neuropsychological function of ADHD.

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