Diagnostic Utility of Conners Continuous Performance Test-3 for Attention Deficit/Hyperactivity Disorder: A Systematic Review

Abstract

Objective:

To review the literature on the utility of the Conners CPT-3 in persons with ADHD.

Methods:

A systematic review was conducted. Six databases were searched using inclusion criteria: research studies, year 2000+, English, and ages 8+. Two raters independently screened 1,480 title/abstracts and subsequently reviewed 399 full texts. Data extraction and critical appraisal were conducted. Reflective thematic analysis through inductive coding identified qualitative themes.

Results:

Thirteen studies met inclusion criteria with five themes identified. Five studies found CPT-3 was a weak or poor predictor of ADHD diagnosis while two found it was an adequate predictor. Two studies found CPT-3 could differentiate clients with comorbid ADHD/anxiety from ADHD or ADHD from obsessive-compulsive disorder. One found CPT-3 could not differentiate ADHD from ASD or comorbid ADHD/ASD.

Conclusions:

Results revealed CPT-3 as a standalone measure is a weak or poor predictor of ADHD. Multiple measures for evaluating persons with ADHD are recommended.

Keywords

Conners Continuous Performance Test 3 diagnosis attention deficit/hyperactivity disorder systematic review

Introduction

ADHD is a neurobehavioral disorder that affects 4% to 8% of school age children (Erskine et al., 2016) and symptoms persist into adulthood for 60% of adults (Faraone et al., 2006; Kessler et al., 2006). Three core symptoms are associated with ADHD: inattention, impulsivity/disinhibition, and hyperactivity (Barkley, 2018). Secondary deficits are seen in slow processing speed, weaker working memory functioning, and disruptions in reaction time (Barkley, 2018; Mohamed et al., 2021). According to some scholars and the American Academy of Pediatrics (Wolraich et al., 2019), a diagnosis of ADHD can be made based on clinical interview and behavioral rating scales alone (Barkley, 2018). However, several studies and systematic reviews have demonstrated the utility of continuous performance tests (CPTs) to differentiate children and adults with ADHD from those with other diagnoses and from healthy controls (Epstein et al., 2003; Frazier et al., 2004; Hall et al., 2016; Losier et al., 1996; Slobodin, 2020). Subsequently, Ogundele et al. (2011) suggested that adding objective measures such as CPTs and rating scales increases the accuracy of the interview-based diagnostic process with individuals with ADHD.

CPTs are one of the most frequently used instruments to assess aspects of attention and impulsivity (Edwards et al., 2007; Fuermaier et al., 2019; Riccio & Reynolds, 2006; Slobodin, 2020) and are often included in batteries to identify the presence of ADHD symptoms (Barkley, 2018; Lezak, 2012). Rosvold et al. (1956) developed the first CPT for detecting attentional problems in persons with a brain injury. Since then, CPT instruments have been used to measure aspects of attention (selective and sustained) and impulsivity/inhibition in several clinical groups and research topics. Numerous variations of the paradigm exist (Riccio et al., 2002), but most CPTs are computer-based programs which involve rapid presentation of visual, and at times, auditory stimuli for detection and response. Typically, the subject is asked to respond to a predetermined target upon presentation and to withhold a response to non-target presentations. CPTs posit that declines in performance are a result of the “strictness of the response criterion” and not due to changes in a child’s ability to detect a target (Swanson, 1983).

Evolution of the Conners CPT

There have been three iterations of the Conners Continuous Performance Test (Conners, 1994, 2000, 2014). The original Conners CPT altered how stimuli were presented to the examinee compared to previously developed CPTs. Traditionally, CPTs had used an A (alert) X (target) format, where examinees pressed a button each time the AX combination appeared, which occurs infrequently. Conversely, in the Conners CPT, the examinee was required to press a button every time a letter appeared on the screen except when the letter X appeared. In the case of the appearing X, the examinee was to refrain from hitting the button. Conners reported that this approach produced more responses and was a better measure of impulsivity (Riccio et al., 2001).

The CPT was revised in 2000 to the CPT-2 (Conners, 2000). There is an abundance of research on the first two editions of the Conners CPT with various diagnostic groups (De Magalhães Narvaez et al., 2014; Edwards et al., 2007; Epstein et al., 2003; Fasmer et al., 2016; Hall et al., 2016; Losier et al., 1996; Lundervold et al., 2016; Slobodin, 2020; Wang et al., 2011). Hall et al. (2016) reviewed a number of commercially available continuous performance tests, including the CPT-2, to determine sensitivity to and accuracy in detecting the presence of ADHD in children. They found Conners CPT-2 was useful in successfully diagnosing persons with ADHD. On the other hand, Slobodin (2020) conducted a systematic review of articles pertaining to the ability of various CPTs to differentiate persons with substance use disorder from those with ADHD. They found the CPT 2 could not differentiate adults with ADHD from adults with substance use disorder. Overall, further research is needed to address the inconsistency in whether the CPT-2 could effectively differentiate between people who had ADHD from other diagnostic groups.

The current version of the Conners CPT, the CPT-3, was revised to improve its usability, strengthen the psychometric properties, and update the normative base (Conners, 2014). The CPT-3 is a 14-minute, computerized test normed for persons 8 to 70 years old. Over the duration of the test, 324 target stimuli appear (letters). The subject is asked to press the spacebar when the target letters appear. The letter “X” appears 36 times resulting in a frequency of target to non-target ratio of 11%. This type of stimuli response paradigm is referred to as a “non-X paradigm,” which means that the individual is reviewing target stimuli for most of the test and must inhibit their response when the non-target stimuli appear. Consequently, the CPT-3 is classified as an inhibitory control paradigm test (Huang-Pollock et al., 2012). Nine scores are derived from performance on the CPT-3 (Table 1).

Table 1.

Conners CPT-3 Measures.

CPT measure	Interpretation
Detectability (d′)	Measures the ability to differentiate non-targets (the letter X) from targets (all other letters).
Omission errors (OMI)	Measures the number of targets missed across trials with higher scores being indicative of inattention during the task
Commission errors (COM)	Measures the number of incorrect responses to non-targets and high scores are associated with either inattentiveness or impulsivity/disinhibition
Perseveration scores (PER)	Measures anticipatory responses outside of context, slow responses to a previous stimulus, or random responding with a high score indicating impulsivity.
Hit reaction time (HRT)	The mean response time for all non-perseverative responses during the entire test. Atypical HRT scores indicate inattentiveness.
HRT-standard deviation (HRT-SD)	Measures the consistency of response speed to targets for the entire administration. High HRT-SD means greater inconsistency response speed which is indicative of inattentiveness
HRT block change (HRT-BC)	Measures subject’s attention and information processing speed efficiency varied throughout the administration. HRT-BC is the slope of the change in the HRT across the six blocks of administration. Higher scores indicate that the subject’s processing speed slowed considerably over the course of the test (negative slope) due to a loss of sustained attention.
HRT inter-stimulus interval (HRT-ISI) change	Either means the subject’s information processing efficiency declined with longer pauses between the stimulus presentations in a block resulting in a loss of vigilance OR the subject struggled to meet task demands and changes
Variability (VAR)	Measures responding speed consistency, however, it is a within respondent measure meaning the amount of variability the respondent showed during the 18-seperate sub-blocks.

Changes to the CPT-3 included cosmetic changes to the presentation of stimuli (i.e., more contrast between letters and the background), adding a practice section to reduce errors due to lack of familiarity with the task demand, and establishing 8-years-old as the minimum age for administration. Finally, the proportion of non-target items was changed from 10% to 20% to increase the psychometric properties of the COM measure.

The CPT-3 has received mixed reviews. The developers of the CPT-3 opine that the test is a sensitive measure of impulsivity and disinhibition associated with attention. The test also breaks the task into different interstimulus intervals (ISIs) or blocks of stimuli presentation during which the stimuli presentation speed changes, thus reducing the subject’s ability to adjust to the pace of the task (Conners, 2014). Others have criticized the ISI change in speed of presentation on anatomical grounds indicating that the changing of the speed of the stimulus presentation actually alerts the arousal system and re-engages the attentional (specifically vigilance) system (Koziol & Budding, 2010). Nonetheless, Dombrowski and Gischlar (2017) reviewed the CPT-3 and concluded it is a good addition to a battery of psychometric and self-report instruments in the assessment of persons with ADHD from those without ADHD. Other studies have focused on the use of the CPT-3 on diagnosing specific co-occurring disorders with mixed results (Baggio, Hasler, Deiber, et al., 2020; Baggio, Hasler, Giacomini, et al., 2020; Fuermaier et al., 2019). More recently, Pagán et al. (2023) conducted a systematic review of the Conners CPT family of tests to determine diagnostic utility with adults who are suspected of meeting ADHD criteria. However, 91% of their sample focused on outdated versions of the test (i.e., 60% CPT-2, 31% CPT) and only 9% of the sample used the CPT-3.

Study Objective

The aim of this study is to review the recent literature on the CPT-3 and its ability to contribute to the diagnosis of ADHD in children/adolescents and adult populations. Our review included only studies using data derived from the CPT-3. To our knowledge, a systematic review of the utility of the CPT-3 to diagnose a person with ADHD has not been conducted.

Methods

This systematic review follows the recommended Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) standards (Page et al., 2021). A review protocol was not registered for this study.

Inclusion/Exclusion Criteria Development

Initial scoping searches revealed an interesting challenge when determining the inclusion and exclusion criteria for this review. Many published studies did not identify the CPT test by name or indicate which test version was used in article titles or abstracts. Instead, many studies simply stated they used a continuous performance test/task, neglecting to identify “Conners” or other CPT tests by name; or stated “Conners CPT” but not the specific version such as CPT-2 or CPT-3. In order to perform a comprehensive search, the inclusion criteria set a wide publication date range to include studies that may have used the CPT-2 or CPT-3. This was also important given that many recent studies still reported using the CPT-2 in clinical practice and research, despite the CPT-3 being released in 2014.

To be considered for inclusion in this systematic review, studies must have (a) been a primary research study, inclusive of dissertations; (b) used the Conners CPT-3 as a diagnostic or assessment tool of ADHD; (c) published in the year 2000 or later; and (d) published in English language. Books and book chapters, secondary research studies (meta-analyses, systematic reviews), and non-research articles (editorials, commentaries, narrative reviews) were excluded. Studies that used the original Conners CPT, CPT-2, or other CPT tests were excluded as well as studies that only addressed vague “attention issues” and not ADHD specifically.

Search Strategy

Literature searches were conducted by the information professional team member (SMS) in January 2022 and again in October 2022 to capture newly published articles. Six bibliographic databases were searched: PsycINFO (EBSCO), PsycARTICLES (EBSCO), MEDLINE (PubMed), Psy-chology and Behavioral Science Collection (EBSCO), ProQuest (collection of 14 multidisciplinary databases including Psychology Database, Social Sciences Database, Nursing and Allied Health Premium, and Dissertations & Theses), and Google Scholar. Given the challenge stated above with inconsistent reporting of CPT test names and versions in articles, search terms were purposely broad to capture any studies related to continuous performance tests/tasks rather than utilizing terms for Conners CPT only. A combination of keywords and database-specific subject headings were used such as “continuous performance,” CPT, CCPT, attention deficit, inattention, and “Attention Deficit Disorder with Hyperactivity” [Mesh]. Some searches only searched the title, abstract, and subject heading fields while others searched in all fields. Language and publication date filters were also applied (Appendix A1). Duplicate records were removed using Zotero citation management software. In addition, the Conners CPT-3 Manual (Conners, 2014) was hand searched for any relevant references.

Screening Process

A pilot title/abstract screening of 20 randomly selected studies from the search was conducted by all members of the research team to ensure inter-rater reliability and interpretation of the inclusion and exclusion criteria. Title/abstract screening then commenced using Rayyan (https://www.rayyan.ai/). Each study was independently reviewed by two members of the research team and any discrepancies were resolved by a third member. Following title/abstract screening, full text screening of the remaining studies occurred with the same procedure with any differences being resolved by a third reviewer.

Data Extraction & Critical Appraisal

A data extraction template was developed by the team, and all team members independently piloted the template using two of the included studies. The team then met to review and compare data extraction consistency. Adjustments were made to the template and the format and extent of data extracted was agreed upon by all members (Appendix B1). Extracted data included: publication information, study methodology, participant characteristics (ages, diagnoses, comorbidities), sample size, variables tested, and statistical methods. In addition, specific data related to CPT-3 test outcomes in four domains was extracted:

attention (d′, omission errors, commission errors, HRT, HRT-SD, variability)

sustained attention (HRT, HRT-BC, omissions by block, commissions by block)

vigilance (HRT-ISI Change, omissions by ISI, commissions by ISI)

impulsivity (commission errors, perseveration scores, HRT, HRT-SD)

Following the pilot, two team members were assigned to each study: one as primary reviewer and one as secondary reviewer and reviewed independently. Any differences in the data extracted were resolved by a third reviewer.

Critical appraisal of included studies was conducted to assess study strengths, weaknesses, and potential biases using the Quality Assessment Tool for Quantitative Studies (Thomas et al., 2004). Again, two team members were assigned to each study, one as primary reviewer and one as secondary reviewer with a third reviewer resolving any discrepancies. This tool was selected as all included studies used quantitative methods, but a variety of study designs; therefore ratings and limitations could be easily compared across designs.

Study Synthesis and Analysis

The quantitative CPT-3 outcomes data extracted from each study was compared to determine if it could be quantitatively combined for meta-analysis. Several studies did not report (or inconsistently reported) data such as means and standard deviations. The team contacted the authors of studies to request additional data, but did not acquire sufficient data to perform meta-analysis or examine the data through alternative statistical analysis methods.

Therefore, the qualitative data extracted from the studies was analyzed using reflexive thematic analysis through inductive coding. In reflexive thematic analysis, themes are generated by immersing oneself in the data through consecutive reading, discussion, and rereading of the dataset (Braun & Clarke, 2006, 2019). In this study, two team members (PDC and SMS) participated in the thematic analysis process. The 13 studies were first independently reviewed by each member; each generating a list of potential themes. The team then met to discuss and compare their observations and reflections of the patterns in the studies and agree on the emerging themes. Articles were then reread to apply the set of themes to the 13 studies. Synthesis and interpretation of the themes are reported in the following section.

Results

Study Selection

A total of 2,796 records were captured across the initial and final searches (2,642 in January 2022 and another 154 in October 2022). After removal of duplicates, 1,480 records were screened by title/abstract followed by 399 records screened by full text. Thirteen studies met the inclusion criteria and were included in the review (Figure 1; Table 2).

Figure 1.

Flow diagram.

Table 2.

Summary of Included Studies (N = 13).

Author (year)/country	Source	Sample size (groups)	Demographics			Diagnosis criteria	CPT3 elements reported										Other psychological assessments	Other medical assessments	Theme(s)	Assessment of study quality and risk of bias
Author (year)/country	Source	Sample size (groups)	Gender	Age	Ethnicity	Diagnosis criteria	OM I	COM	V AR	d′	P ER	RS	HR T	HR TSD	HR TBC	HR T I S I	Other psychological assessments	Other medical assessments	Theme(s)	Assessment of study quality and risk of bias
Baggio, Hasler, Giacomin et al. (2020)/Switzerland	Journal of Attention Disorders	N = 201	Male n = 54.7; female n = 45.3	Mean age = 35.2 years old	Ethnicity not described	• Diagnostic Interview for Adult ADHD (DIVA) 2.0	X	X	X	X	X	X	X	X	X	X	• Diagnostic Interview for Adult • Self-reported measures of ADHD • Adult ADHD Self-Report Scale (ASRS) • Wender Utah Rating Scale (WURS)		CPT-3 as a Predictor of ADHD from Healthy Controls	Overall Assessment: Moderate Quality and Risk of Bias.Moderate selection bias, moderate study design, weak confounders, moderate blinders, moderate data collection, weak withdrawals and dropouts
Baggio, Hasler, Deiber, et al. (2020)/Switzerland	Psychiatry Research	N = 66	Male n = 36, female n = 30;	Mean age = 27.9 years old	Ethnicity not described	• Diagnostic and Statistical Manual of Mental Disorders - 5th Edition (DSM-5)• DIVA• ADHD Child Evaluation (ACE+)	X	X	X	X				X			• Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV)		CPT-3 as a Predictor of ADHD from Healthy Controls	Overall Assessment: Moderate Quality and Risk of Bias.Weak selection bias, moderate study design, weak confounders, weak blinders, strong data collection, strong withdrawals and dropouts
Chang et al. (2022)/Taiwan	Journal of Neural Engineering	N = 60;(ADHD: n = 30 and NT: n = 30)	ADHD:male n = 24female n = 6NT:male n = 22female n = 8	ADHD:mean age =80 monthsNT:mean age = 77.03 months	All participants were Taiwanese	No diagnostic criteria was given: assessed and confirmed by at least two pediatricians in clinical settings	X	X	X	X	X		X	X	X	X	• Conners Kiddie Continuous Performance Test-second edition (K-CPT2)	Electroencephalogram test (EEG)	CPT-3 as a Predictor of ADHD from Healthy Controls; CPT-3 Correlates with Brain Regions	Overall Assessment: Weak Quality, High Risk of Bias.Weak selection bias, moderate study design, weak confounders, weak blinding, strong data collection methods, weak withdrawals and drop-outs indication
Derbyshire (2020)/USA - Illinois	Dissertation - Rosalind Franklin University	N = 86	OCD group: male n = 17 female n = 11ADHD group: male n = 15female n = 14NC group:male n = 14 female n = 15	8–15 years old	Ethnicity not described	• DSM-5	X	X	X				X		X	X	• Child Behavior Checklist (CBCL)• Multidimensional Anxiety Scale for Children-2nd Edition (MASC-2)• Wechsler Intelligence Scale for Children IV and V (WISC IV and V)		CPT-3 Ability to Differentiate ADHD from ADHD w/Psychiatric Comorbidities	Overall Assessment: Moderate Quality and Risk of Bias.Moderate selection bias, moderate study design, weak confounders, moderate blinders, strong data collection, strong withdrawals and dropouts, moderate global rating
Dunbar et al. (2021)/USA - Texas	Journal of Attention Disorders	ADHD N = 101(epilepsy: n = 54, PNES: n = 22)	Male n = 33	18–78 years old; mean age = 38.4 years old	Ethnicity not described	• Adult ADHD Self-Report Scale (ASRS) and/or CPT	X	X	X	X	X	X	X	X	X		• Adult ADHD Self-Report Scale (ASRS)		CPT-3 Correlates with Brain Regions	Overall Assessment: Moderate Quality and Risk of Bias.Moderate selection bias, moderate study design, moderate confounders, weak blinders, strong data collection, strong withdrawals and dropouts, moderate global rating
Fuermaier et al. (2022)/The Netherlands,Germany, Ireland	International Journal of Environmental Research and Public Health	N = 31(ADHD n = 31 and comorbid mood disorders: n = 11 and comorbid anxiety: n = 1)	Male n = 17, female n = 14	Mean age = 37.29 years old	Ethnicity not described	• DSM-5• Essen-Interview-for-school days-related biography• Wender-Reimherr Interview	X	X						X			• Beck Depression Inventory-II (BDI-II)• Cambridge Neuropsychological Test of Automated Batter-Rapid Visual Processing (CANTAB-RVP)• Conners Adult ADHD Rating Scales (CAARS)• Integrated Visual and Auditory Continuous Performance Test (IVA-2)• Test Battery for Attention Performance-Vigilance (TAP)• Test of Everyday Attention (TEA)• Tests of Variables of Attention (TOVA)• VIGIL/Vienna Test System (VTS)• Perception and Attention Functions-Vigilance (WAFV)• Weiss Functional Impairment Rating Scale-self report (WFIRS-S)• Wender Utah Rating Scale (WURS-K) German short version		CPT-3 Compared to Other CPT Measures	Overall Assessment: Weak Quality, High Risk of Bias.Weak selection bias, moderate study design, weak confounders, weak blinding, strong data collection methods, weak withdrawals and drop-outs indication
Gagnon et al. (2022)/Canada	Cerebral Cortex	N = 171(ADHD: n = 27 and ASD: n = 3)	Male n = 104, female n = 67;	Mean age = 11.33 years old	Ethnicity not described	• DSM-5	X	X	X	X	X		X	X	X	X		Diffusion magnetic resonance imaging test (dMRI)	CPT-3 Correlates with Brain Regions	Overall Assessment: Weak Quality, High Risk of Bias.Weak selection bias, moderate study design, weak confounders, moderate blinders, weak data collection, strong withdrawals and dropouts
Keroles (2022)/USA - Pennsylvania	Dissertation - Widener University	N = 45(ADHD only: n = 31 and ADHD+ anxiety: n = 14)	Male n = 27; female n = 18	10–17 years old	Ethnicity not described	• DSM-5	X						X	X	X		• Behavior Assessment System for Children-2nd/3rd Editions (BASC-2/3)• Multidimensional Anxiety Scale for Children-2nd Edition (MASC-2)		CPT-3 Ability to Differentiate ADHD from ADHD w/Psychiatric Comorbidities	Overall Assessment: Weak Quality, High Risk of Bias.Moderate selection bias, weak study design, weak confounders, strong blinders, strong data collection, moderate withdrawals and dropouts
Ord et al. (2021)/USA –North Carolina	Clinical Neuropsychologist	N = 197(ADHD: n = 107)	Male n = 167female n = 30	Mean age = 38.62 years old	White n = 160, Black n = 28, Hispanic n = 6, Other n = 3	No diagnostic criteria was given	X	X			X		X	X		X	• Test of Memory Malingering (TOMM)		CPT-3 as a Performance Validity Indicator Test	Overall Assessment: Strong Quality, Low Risk of Bias.Moderate selection bias, moderate study design, moderate confounders, strong blinders, strong data collection, strong withdrawals and dropouts
Robinson et al. (2022)/USA - Louisiana	Journal of Attention Disorders	N = 201(credible group (failed 0 PVTs): n = 159 and non-credible group (failed >2 PVTs): n = 42)	Credible group:male n = 81 female n = 76Non-credible group:male n = 13 female n = 29	Credible group: mean age = 23.04 years oldNon-credible group:mean age = 22.93 years old	Credible group: White n = 127Black n = 18 Hispanic n = 38 Asian n = 9Other n = 5Non-credible group:White n = 29Black n = 9Hispanic n = 8 Asian n = 3Other n = 1	Comprehensive diagnostic assessment included: clinical interviews, cognitive and academic measures, and several freestanding and embedded PVTs.	X	X	X	X	X		X	X	X	X			CPT-3 as a Performance Validity Indicator Test	Overall Assessment: Weak Quality, High Risk of Bias.Weak selection bias, moderate study design, weak confounders, weak blinding, weak data collection methods, weak overall rating
Scimeca et al. (2021)/USA - Illinois	Developmental Neuropsychology	N = 201(valid group: n = 169 and invalid group: n = 32)	Valid group: male n = 61 female n = 108Invalid group:male n = 20 female n = 11	Valid group: mean age = 27.78 years oldInvalid group: mean age = 26.84 years old	Valid group:White n = 80Black n = 21Hispanic n = 38 Asian n = 16Other = 14Invalid group: White n = 14Black n = 7Hispanic n = 8 Asian n = 3Other n = 0	• DSM-5• Clinical Assessment of Attention Deficit (CAT-A)	X	X	X	X	X	X	X	X	X	X	• Brief Visuospatial Memory Test-Revised Recognition Discrimination (BVMT)• Dot Counting Test E-Score (DCT)• Rey Auditory Verbal Learning Test Effort Score (RAVLT)• Reliable Digit Span Forward and Backward (RDS)• Rey 15 Test + Recognition (RFIT)• Stroop Color Word Test Word Reading T-Score (SCW)• Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV)		CPT-3 as a Performance Validity Indicator Test	Overall Assessment: Strong Quality, Low Risk of Bias.Moderate selection bias, moderate study design, moderate confounders, strong blinders, strong data collection, strong withdrawals and dropouts
Shahabuddin (2018)/USA - Michigan	Dissertation - Eastern Michigan University	N = 16	ASD only group:male n = 12 female n = 4ADHD only group:male n = 9 female n = 7ASD + ADHD group:male n = 15 female n = 1TD group:male n = 9female n = 7	ASD only group:mean age = 9.68 years oldADHD only group:mean age = 8.89 years oldASD + ADHD group:mean age = 9.71 years oldTD group: mean age = 9.37 years old	ASD only group: White n = 13Black n = 2Asian n = 1ADHD only group:White n = 14Black n = 1 Hispanic n = 1ASD + ADHD group:White n = 11Black n = 3 Hispanic n = 1 Asian n = 1TD group:White n = 14Asian n = 2	• DSM-5	X	X					X		X		• Conners 3 Behavior Rating Scale		CPT-3 Ability to Differentiate ADHD from ADHD w/Psychiatric Comorbidities	Overall Assessment: Moderate Quality and Risk of Bias.Moderate selection bias, moderate study design, moderate confounders, moderate blinders, strong data collection, strong withdrawals and dropouts
Wang et al. (2021)/Republic of China	Journal of Attention Disorders	N = 165(ADHD: n = 107 and healthy controls: n = 58)	ADHD group:male n = 84 female n = 23Healthy Control group:male n = 35 female n = 23	6–16 years oldADHD group mean age = 9.6Healthy Control group:mean age = 9.9	All participants were Han Chinese	• DSM-IV-TR• Schedule for Affective Disorders and Schizophrenia for School-Age Children-epidemiologic version (K-SADS-E) Chinese version	X	X	X	X	X	X	X	X			• Conners Auditory Test of Achievement (CATA)• Schedule for Affective Disorders and Schizophrenia for School-Age Children-epidemiologic version (K-SADS-E) Chinese version• Wechsler Intelligence Scale for Children IV (WISC-IV) Chinese version• Swanson, Nolan, and Pelham Rating Scale (SNAP-IV)		CPT-3 as a Predictor of ADHD from Healthy Controls	Overall Assessment: Weak Quality, High Risk of Bias.Moderate selection bias, strong study design, strong confounders, weak blinding, strong data collection methods, weak withdrawals and drop-out indication

Study Characteristics

The 13 included studies were published between 2018 and 2022 with seven conducted in the United States (Derbyshire, 2020; Dunbar et al., 2021; Keroles, 2022; Ord et al., 2021; Robinson et al., 2022; Scimeca et al., 2021; Shahabuddin, 2018), two in Switzerland (Baggio, Hasler, Deiber, et al., 2020; Baggio, Hasler, Giacomini, et al., 2020), and one each in China (Wang et al., 2021), Taiwan (Chang et al., 2022), and Canada (Gagnon et al., 2022), respectively. In addition, one study was a cross-country collaboration in Europe with authors from Germany, The Netherlands, and Ireland (Fuermaier et al., 2022). Ten were primary research studies (Baggio, Hasler, Deiber, et al., 2020; Baggio, Hasler, Giacomini, et al., 2020; Chang et al., 2022; Dunbar et al., 2021; Fuermaier et al., 2022; Gagnon et al., 2022; Ord et al., 2021; Robinson et al., 2022; Scimeca et al., 2021; Wang et al., 2021) and three were dissertations (Derbyshire, 2020; Keroles, 2022; Shahabuddin, 2018). The following study designs were represented: four retrospective studies (Derbyshire, 2020; Gagnon et al., 2022; Keroles, 2022; Ord et al., 2021), three cross-sectional studies (Baggio, Hasler, Deiber, et al., 2020; Baggio, Hasler, Giacomini, et al., 2020; Scimeca et al., 2021), three case controls (Chang et al., 2022; Robinson et al., 2022; Wang et al., 2021), two quasi-experimental (Dunbar et al., 2021; Shahabuddin, 2018), and one prospective (Fuermaier et al., 2022). Of these, five studies focused on children and adolescents (Chang et al., 2022; Derbyshire, 2020; Gagnon et al., 2022; Keroles, 2022; Shahabuddin, 2018) and eight studies focused on adults aged 18 and over (Baggio, Hasler, Deiber, et al., 2020; Baggio, Hasler, Giacomini, et al., 2020; Fuermaier et al., 2022; Ord et al., 2021; Robinson et al., 2022; Scimeca et al., 2021; Wang et al., 2021). Six of these studies looked at the CPT-3’s ability to distinguish ADHD from healthy controls (HC) (Baggio, Hasler, Deiber, et al., 2020; Baggio, Hasler, Giacomini, et al., 2020; Chang et al., 2022; Fuermaier et al., 2022; Scimeca et al., 2021; Wang et al., 2021). Seven studies looked at the ability of the CPT-3 to distinguish ADHD from ADHD+ comorbid psychiatric disorders/medical disorders (Derbyshire, 2020; Dunbar et al., 2021; Gagnon et al., 2022; Keroles, 2022; Ord et al., 2021; Robinson et al., 2022; Shahabuddin, 2018) with the most common comorbidities being anxiety disorder (Baggio, Hasler, Giacomini, et al., 2020; Dunbar et al., 2021; Keroles, 2022; Ord et al., 2021), autism spectrum disorder (Gagnon et al., 2022; Shahabuddin, 2018), bipolar disorder (Baggio, Hasler, Giacomini, et al., 2020; Ord et al., 2021), major depressive disorder (Baggio, Hasler, Giacomini, et al., 2020; Ord et al., 2021), and substance use disorders (Baggio, Hasler, Giacomini, et al., 2020; Ord et al., 2021), among others.

Five themes were identified through the analysis (Figure 2):

CPT-3 as a Predictor of ADHD from Healthy Controls (n = 4)

CPT-3 Ability to Differentiate ADHD from ADHD w/Psychiatric Comorbidities (n = 3)

CPT-3 Compared to Other CPT Measures (n = 1)

CPT-3 as a Performance Validity Indicator Test (PVT) (n = 3)

CPT-3 Correlates with Brain Regions (n = 3)

Figure 2.

Included studies by CPT-3 theme (N = 13).

Theme 1: CPT-3 as a Predictor of ADHD from Healthy Controls

Four studies assessed the predictive value of identifying participants with ADHD from healthy controls (Baggio, Hasler, Deiber, et al., 2020; Baggio, Hasler, Giacomini, et al., 2020; Chang et al., 2022; Wang et al., 2021) with mixed results. They are reported here by those with positive and negative findings.

Positive findings

Two studies found the CPT-3 could differentiate persons with ADHD from healthy controls (HC) (Chang et al., 2022; Wang et al., 2021). Wang et al. (2021) were interested in the discriminative validity of visual and auditory attention tests to differentiate ADHD from HC. In all, 165 individuals were recruited. Both groups were comparable in age (ADHD group mean age = 9.6, HC mean age = 9.9). All participants were administered CPT-3 and the Conners Auditory Test of Attention (CATA). They concluded that although both the CPT-3 and the CATA predicted ADHD individually, together, they were a better predictor of ADHD than either one alone. Wang found the following CPT-3 indices to be significant predictors: OMI, COM, HRT-SD, VAR, d′, and PER.

Chang et al.’s (2022) primary hypothesis was that EEG data could be helpful in diagnosing persons with ADHD and differentiating those with ADHD from HC. They further proposed changes in EEG-based long short-term memory (LSTM) networks would contribute to the differentiation of children with ADHD from HC. A total of 30 children classified as HC and 30 children with ADHD were administered the CPT-3 while being monitored with EEG. They found the CPT could differentiate ADHD from HC on the following variables: d′, OMI, HRT, HRT-SD, HRT-ISI, and VAR. Significant biomarkers were extrapolated from the visualization of the individual features of the EEG that was a significant contributor to classification and differentiation of children with ADHD from HC. However, the results of Chang et al. (2022) are questionable based on methodological grounds. The authors blended the Kiddie CPT-2 and the CPT-3 results without providing any information about the number of participants that actually were administered either instrument. The age range of participants spanned age 4 to age 8.4. The mean age for the two groups was 6.4 and 6.6 which is well below the cutoff for the CPT-3 at age 8. Consequently, these results are more applicable to the Kiddie CPT-2 than the CPT-3 and are not germane to this investigation.

Negative findings

Two studies found the CPT-3 to be a weak or poor predictor of ADHD from HC (Baggio, Hasler, Deiber, et al., 2020; Baggio, Hasler, Giacomini, et al., 2020).

Baggio, Hasler, Giacomini, et al. (2020) found no evidence that the CPT-3 indices could discriminate between adults with ADHD and HC. The classification error was 80.3% for the ADHD-Inattentive type and 22.5% for the ADHD-Combined type. They did report a small correlation with the following variables: OMI, COM, HRT-SD, and VAR; only one of these indices correlated greater than r = .30 (maximum of common variance = 9.6%). In their report, this meant that ADHD symptoms severity was negligibly related to CPT-3 variables. Further, all CPT-3 scores fell in the average range for typical HC controls (T scores from 45 to 60). Baggio, Hasler, Giacomini, et al. (2020) concluded that the CPT-3 lacks specificity due to the high rate of false positive findings in children, which is similar to the conclusions of Riccio and Reynolds (2006) who expressed similar concerns regarding the specificity of CPT tests in general in all populations.

Baggio, Hasler, Deiber, et al. (2020) found that all CPT-3 indices were moderated by intellectual function (IQ) with adults with higher IQ levels (>109) performing better on the CPT-3 than persons with lower IQ scores (<110). They concluded that persons with high IQ have more cognitive reserve, meaning better sustained attention and inhibition ability due to the elevated IQ.

Theme 2: CPT-3 Ability to Differentiate ADHD from ADHD w/Psychiatric Comorbidities

Three studies assessed the ability of the CPT-3 to differentiate those with ADHD alone compared to those with comorbid ADHD and other psychiatric disorders (Derbyshire, 2020; Keroles, 2022; Shahabuddin, 2018).

Keroles (2022) investigated the ability of the CPT-3 to differentiate between a group of 31 adolescents (ages 10–17, mean age = 13.51 SD = 2.22) with ADHD alone from a group of 14 adolescents with ADHD and a comorbid anxiety disorder (ADHD+). Females composed 31.3% of the ADHD alone group and 57.1% of the ADHD+ group. Keroles found that four indices could differentiate adolescents with ADHD from adolescents with ADHD+: OMI, HRT, HRT-SD, and HRT-BC. However, only HRT-BC made a unique contribution to the regression formula while noting that HRT-SD fell just outside of the significance range at p = .06. They found that as T scores rose on the HRT-SD measure adolescents were more likely to be diagnosed with ADHD alone. Alternatively, as the HRT-BC T scores increased, the adolescents were more likely to be diagnosed with ADHD+. Questions about the power of the study loom with such a small sample size. The higher proportion of females in the sample may limit the generalizability to adolescent males.

Shahabuddin (2018) examined sustained and selective attentional processing in children ages 8 to 10 years with autism spectrum disorder (ASD) only (n = 16), ADHD only (n = 16), comorbid ASD and ADHD (n = 16), and typically developing (TD; n = 16) controls. The authors concluded that omission and commission scores have some clinical utility to differentiate children with ADHD from ASD; but not from children with combined ADHD and ASD. They also concluded that the ability of the CPT-3 to differentiate children with ADHD from children with ASD based on the HRT and HRT-BC variables was not statistically supported. A weakness in this study is the small number of participants in each group.

Derbyshire (2020) study aimed to evaluate the attentional processes in youths with OCD by comparing performance on a continuous performance task to youths with ADHD and a non-clinical control group. Eighty-six children ages 8 to 15 were assigned to one of three groups (OCD = 28), (ADHD = 29) and (H = 29). Conclusions for this study indicated statistically significant differences on the following CPT-3 measures: OMI, COM, HRT, VAR, HRT-BC, and HRT-ISI. More variability within scores was found in the ADHD group as compared to the OCD group on Blocks 2, 3, 4, and 5. There was no difference between groups on Blocks 1 and 6. The authors conclude that the CPT-3 can be used to differentiate children with ADHD from children with OCD and from HC.

In analyzing the three studies, two of the three studies found that the CPT-3 could not differentiate children with ADHD from children with either comorbid ASD/ADHD or comorbid OCD/ADHD (Derbyshire, 2020; Shahabuddin, 2018). One study found that the CPT-3 could differentiate children with ADHD from those with ADHD and comorbid anxiety disorders (Keroles, 2022). The authors of that study noted a limitation in having a low but minimally acceptable number of participants in the ADHD alone group, but an inadequate number of participants in the ADHD+ anxiety group, which could have impacted the power of the study. All three studies used older children or adolescents as their participants meaning the results of the studies are only generalizable to 10- to 15-year olds.

Theme 3: CPT-3 Compared to Other CPT Measures

One study was identified with this theme (Fuermaier et al., 2022). Fuermaier et al. (2022) compared eight CPTs on their ability to diagnose 31 adults with ADHD: CPT-3, Test of Variable Attention (TOVA), Cambridge Neuropsychological Test-Automated (CANTAB), Test of Everyday Attention (TEA), Integrated Visual and Auditory Continuous Performance Test (IVA-2), Test Battery for Attention (TAP), Perception and Attention Functions-Vigilance (WAFV), and Vienna Tests Systems-Vigilance (VIGIL). An examination of the several types of outcome variables (e.g., response time, variability of response time, or accuracy of responses) did not suggest any particular type of outcome variable to be more sensitive in revealing ADHD symptoms than others. Only 20% of ADHD patients showed minor elevations on OMI and COM scores on the CPT-3. No indices from the CPT-3 were found to be sensitive for cognitive impairment. They concluded that the ability of the CPT-3 to identify adults with ADHD is weak.

Theme 4: CPT-3 as a Performance Validity Indicator Test (PVT)

Three studies investigated the CPT-3 as a performance validity measure (Ord et al., 2021; Robinson et al., 2022; Scimeca et al., 2021)

Ord et al. (2021) examined embedded validity indicators (EVI) within the CPT-3. In a retrospective chart review from an ADHD evaluation clinic at a Veteran Administration hospital, 197 adult military veterans completed a clinical assessment for ADHD. Eighty-five percent of the participants were male. One-hundred seven (54%) participants were diagnosed with ADHD, 89 were diagnosed with depression, 79 were diagnosed with anxiety, and 59 were diagnosed with trauma. All participants completed the CPT-3 and the Test of Memory Malingering, Trial 1 (TOMM1). Twenty-one percent of the participants failed the TOMM1. The results indicated that veterans who failed TOMM1 made significantly more OMI and COM on the CPT-3, demonstrated poorer discrimination between targets and non-targets (d′), were more inconsistent in their response speed (HRT-SD), and were less efficient at processing changing speed of stimuli presentation (HRT-ISI) than those who passed the TOMM1. The authors note that the previously validated validity indicators identified within the CPT-II do not directly translate to the CPT-3 measure, indicating the need for further investigation.

Similarly, Scimeca et al. (2021) evaluated previously identified CPT-3 scores as EVIs among 201 adults undergoing neuropsychological evaluation for ADHD divided into valid (n = 169) and invalid (n = 32) groups based on seven criterion measures. Patients with ≤1 criterion PVT failure were classified as valid (n = 169; 84%) and those with ≥2 failures as invalid (n = 32; 16%). This study’s aim was to assess the effectiveness of both individual and composite CPT-3 variables for detecting performance invalidity using multiple freestanding and embedded criterion PVTs to establish validity groups (e.g., known groups) among an adult civilian ADHD population. The authors hypothesized that both individual and composite score CPT-3 PVTs, while shown to be robust in mTBI veteran samples (Lange et al., 2013; Ord et al., 2021), would lack the generalizability and psychometric stability to be useful PVTs in a clinical population referred for ADHD evaluation when evaluated using multiple criterion PVTs to establish validity groups. Although six of 10 CPT-3 scores accurately detected invalidity, only two reached minimally acceptable classification sensitivity accuracy of ≥0.70. The remaining four had unacceptably low accuracy (AUCs = 0.62–0.69) with 0.19–0.41 sensitivity at ≥0.90 specificity. The authors conclude that CPT-3 individual and composite scores generally are not accurate PVTs among adults in the general population undergoing clinical evaluation for ADHD. The authors conclude if CPT-3 measures are to be used as PVT, they should be used with other PVT measures due to low sensitivity. These scores would be largely useful to rule in, not rule out invalid performance.

Robinson et al. (2022) investigated the clinical utility of individual and composite indicators within the CPT-3 as EVIs given the discrepant findings of previous investigations. A total of 201 adults undergoing psychoeducational evaluation for ADHD and/or Specific Learning Disorder (SLD) were divided into credible (n = 159), and non-credible (n = 42) groups based on five criterion measures. The findings indicate that although select individual and composite CPT-3 variables may accurately differentiate credible responding from non-credible performance, they may not be appropriate for use in those referred for psychoeducational evaluation given their overall lack of stability. The authors conclude that although select individual and composite CPT-3 variables may accurately differentiate credible responding from non-credible performance, they may not be appropriate as a standalone PVT for use in those referred for psychoeducational evaluation given the CPT-3 overall lack of sensitivity and specificity as a measure of EVI. Clinicians are cautioned when interpreting EVIs on the CPT-3 with individuals presenting for ADHD or SLD.

The three studies together indicate CPT-3 variables as EVIs are weak due to low sensitivity and specificity in community based populations of adults. The CPT-3 may have more validity in veteran populations as a PVT (Scimeca et al., 2021). It is noted that the CPT-3 was not designed to directly measure PVT.

Theme 5: CPT-3 Correlates with Brain Regions

Finally, three studies assessed the correlation between CPT-3 results and neurocognitive disorders (Chang et al., 2022; Dunbar et al., 2021; Gagnon et al., 2022).

Gagnon et al. (2022) focused on exploring which brain network connections are associated with attention, impulsivity, and white matter working memory tracks. They were interested in how alterations in the microstructures within these brain networks related to cognitive functioning. Specifically, they postulated that changes/damage in the microstructures of the working memory fiber networks would be correlated with reduced attentional focus and impulsivity as measured by CPT-3 variables. In all, 171 participants divided into three groups: ADHD, ASD, and no diagnosis were administered a full neuropsychological assessment which included the CPT-3. They were also given a diffusion Magnetic Resonance Imaging study (dMRI) which produces a 3-dimensional model of white matter tracts in the brain. Twenty-seven children met the definition for ADHD and three were diagnosed with ASD. Factor analysis revealed a 2-factor solution; Factor 1 included attentional skills (CPT-3 indices of HRT-SD, VAR, OMI, and HRT-ISI) and Factor 2 measured impulsivity (COM, HRT, d′, and PER). The authors concluded that when looking at the raw score in the CPT3, they did not identify significant differences among the three groups on CPT-3 measures.

Alternatively, Dunbar et al. (2021) focused on identifying ADHD symptoms within persons either with epilepsy or Psychogenic Nonepileptic Seizures (PNES). The purpose of the study was to evaluate the effectiveness of two validated screening tools: the Adult ADHD Self-Report Scale (ASRS) and the CPT-3 in an Epilepsy Monitoring Unit attached to a university hospital. Seventy-six patients were recruited with 54 having a diagnosis of epilepsy and 22 diagnosed with PNES. Patients with PNES were significantly more likely than patients with epilepsy to have had a past history of ADHD, as well as a history of taking medications for ADHD. The results for the ASRS reveal that 40.6% of patients screened positive for ADHD symptoms whereas 63.6% of patients with PNES screened as positive for ADHD symptoms. Positive ASRS screens showed no significant association with positive CPT screens. The authors point out several lines of evidence pointing to the unreliability of the CPT-3 as a screening tool in the distinguishing of ADHD in persons with either epilepsy or PNES. First, the CPT results had a low concordance with the ASRS, consistent with a previous study with similar methodology in adults (Baggio, Hasler, Giacomini, et al., 2020). Second, patients previously diagnosed and treated for ADHD were significantly more likely to score high on the ASRS but not CPT. Third, the CPT only predicted a minimal or moderate likelihood of attention deficits for all four patients with a current ADHD diagnosis. Altogether, these findings suggest that the CPT alone may not be an accurate predictor of attention deficits in this adult population who have epilepsy or PNES.

Finally, Chang et al. (2022), previously reviewed above, had a goal of correlating the CPT-3 results with EEG readouts of brain activity. Although they found a positive association between EEG and CPT score, the identified age range of participants indicated that the Kiddie CPT-2 was a more prominent instrument used than the CPT-3. Therefore the results, although interesting, are not relevant to this review.

In summarizing the three studies, the CPT-3 measures do not appear to be sensitive enough to identify brain regions or brain circuitries associated with neurological disorders and specifically adults with ADHD.

CPT-3 Scores Associated with ADHD Diagnosis by Age

In addition to the thematic analysis described above, the frequency of CPT-3 variables that had a significance level of greater than/equal to .05 level of significance across studies were compiled by age to determine if certain variables were more predictive within age groups. Table 3 reports the findings of four studies with children between ages 8-13. OMI, COM were cited in all four studies. HRT, HRT-SD, HRT-BC, and PER scores were found in two of the four studies.

Table 3.

CPT-3 Variables by Child/Adolescent, Ages 8 to 13 (n = 4).

CPT-3 variable	# of times .05 or > level of p was reported
Omission (OMI)	3
Commission (COM)	3
HRT	2
HRT-SD	2
HRT-ISI	1
HRT-BC	2
Variability (VAR)	1
Perseveration (PER)	2
Detectability (d′)	1

Note. Derbyshire (2020), Keroles (2022), Shahabuddin (2018), Wang et al. (2021) reported correlations and significance levels. Gagnon et al. (2022) did not report correlation and significance values. Chang et al. (2022) was omitted for methodological reasons.

Table 4 reports the findings of six studies with adults age 18+. The OMI was identified in all six studies with adults. The HRT-SD score was identified in five of six studies. The COM and VAR were identified in four of six studies and PER was identified in two of six studies.

Table 4.

CPT-3 Variables by Adult, Ages 18+ (n = 6).

CPT-3 variable	# of times .05 or > level of p was reported
Omission (OMI)	6
Commission (COM)	4
HRT	2
HRT-SD	5
HRT ISI	1
HRT-BC	0
Variability (VAR)	5
Perseveration (PER)	4
Detectability (d′)	5

Note. Baggio, Hasler, Deiber, et al. (2020), Baggio, Hasler, Giacomini, et al. (2020), Dunbar et al. (2021), Fuermaier et al. (2022), Ord et al. (2021), and Scimeca et al. (2021) reported correlation data with significance level. Robinson et al. (2022) and Gagnon et al. (2022) did not report correlation data.

Table 5 reports the CPT-3 variables by all ages in 10 studies by combining Tables 3 and 4. This provides an overview of CPT-3 scores across all ages. Baggio, Hasler, Deiber, et al. (2020); Baggio, Hasler, Giacomini, et al. (2020), Derbyshire (2020), Dunbar et al. (2021), Fuermaier et al. (2022), Keroles (2022), Ord et al. (2021), Scimeca et al. (2021), Shahabuddin (2018), and Wang et al. (2021) were included in the analysis. Chang et al. (2022), Robinson et al. (2022), and Gagnon et al. (2022) did not report correlation data or were excluded due to methodological reasons.

Table 5.

CPT-3 Variables by All Ages (N = 10).

CPT-3 variable	# of times .05 or > level of p was reported
Omission (OMI)	9
Commission (COM)	7
HRT	4
HRT-SD	7
HRT-ISI	2
HRT-BC	2
Variability (VAR)	4
Perseveration (PER)	6
Detectability (d′)	6

When combining the data together a pattern emerges with OMI (nine of 10 studies) and COM, HRT-SD, PER, and d′ (six of 10 studies) scores as the most frequently cited variables. However, the sensitivity and specificity of these variables to diagnose ADHD and to differentiate ADHD from ADHD and other comorbid diagnoses is frequently rated low (Baggio, Hasler, Giacomini, et al., 2020; Dunbar et al., 2021; Fuermaier et al., 2022; Gagnon et al., 2022; Robinson et al., 2022; Scimeca et al., 2021).

Limitations of the review

This systematic review has some limitations. First, to be as inclusive as possible, studies across the globe were included in this review. However, it is unclear whether results can be generalizable due to potential population differences in different countries. Publication bias may also be present due to excluding studies in languages other than English. Second, this review revealed a lack of research specifically on the CPT-3; many studies still report using the CPT-2 despite the CPT-3 being released over 9 years ago. Additional research needs to be conducted to assess the utility of the CPT-3 given the changes implemented from the CPT-2. As a result, this review included a small number of studies and this may reduce the generalizability of the conclusions. It is recommended a follow-up systematic review on this topic be conducted in 5 years. Third, critical appraisal ratings of included studies revealed most studies had an overall weak ranking, indicating a high potential for bias being introduced into the results. This indicates the need for more rigorously designed studies on the CPT-3 to mitigate potential biases and draw stronger conclusions.

Conclusion

ADHD is a common neurobehavioral disorder pervasive across the lifespan. The intended goal of CPTs is to identify symptoms of inattention and inhibition in clinical populations. The aim of this systematic review was to evaluate the utility of the Conners CPT-3 specifically in diagnosing persons with ADHD from healthy controls and ADHD comorbid with other psychiatric disorders. This review identified only a small number of studies using the CPT-3 to evaluate children/adolescents and adults for a diagnosis of ADHD. This impacts the scope and depth of inferences that can be made about the diagnostic utility of the CPT-3. Our limited conclusion based on the studies reviewed is the CPT-3 is an adequate measure to distinguish between persons with ADHD and healthy controls. However, the CPT-3 is not sensitive or specific enough to distinguish between persons with ADHD alone and persons with comorbid ADHD and other psychiatric conditions (ADHD + ASD, ADHD + OCD, ADHD + Anxiety). Although not designed specifically as a PVT measure, compared to other available commercial CPTs, the CPT-3 is not sensitive enough to identify brain regions or brain circuitries implicated in psychiatric disorders. The CPT-3 is not recommended as a stand-alone PVT measure in community based samples, but may have some utility in military hospital evaluations. Finally, the CPT-3 was noncontributory in exploring brain circuitries associated with working memory or attention loops. It is clear from our review that further evaluation is needed to develop a larger pool of studies around the CPT-3 ability to diagnose and distinguish ADHD from other psychiatric disorders.

Mirsky et al. (1991) and Mirsky and Duncan (2004) developed a multicomponent model of attention which specified common clinical measures in the assessment of attention including the use of a CPT. Koziol et al. (2014) verified the utility of the model in the assessment of attention and updated the model to incorporate large scale brain networks. They concluded that the “systematic assessment of these subcomponents of attention was directly related to performance on commonly used neuropsychological instruments with well-established reliability and validity resulting in a battery of tests validated on both child and adult populations across various cultures” (p. 2). Further exploration of this model of assessing attention with the CPT-3 is suggested.

Footnotes

Appendix

Appendix B.

Data Extraction Template.

Publication data
First author	Last Name, First Initial
Publication date	Year (XXXX)
Journal name
Location of study	Country & State (if US)
Purpose
Research question and hypothesis	List all study aims, research question(s), and/or hypotheses
ADHD focus	Identify the focus of the study - is it studying only ADHD or other client comorbidities/disorders?ADHD onlyADHD +
Other diagnoses (if applicable)	List all comorbidities/diagnoses of participants. For example,ADHD, Depression, AnxietyADHD, Autism
Methods
Study methodology	Describe the methodology and identify design if possible (e.g., Randomized controlled trial/RCT, prospective cohort, retrospective cohort, etc.)
Other measures/tests used (if applicable)	Other than the CPT 3, list all other test/measures administered to participants in the study
Was a control or comparison group used in study?	Yes
	No
Participants age	List participant age range with mean and standard deviation
Participant description	Narrative description of participants (e.g., gender (boys, men, etc.), geographic location (Korean children, Brazilian adolescents, etc.), college students, etc.). Check tables and figures for this data
Total sample size (N)	Total participants in the study (N) after exclusion criteria applied
Sample size breakdown (n)	List the sample sizes by each subgroup
Variables tested	List all variables tested in the study
Statistical analysis method(s)	List all statistical analysis methods used (e.g., ANOVA/analysis of variance, MANOVA/multivariate analysis of variance, t-tests, Mann Whitney, regression analysis, Chi-square, Pearson, etc.)
Results
General findings related to CPT	Narrative summary of CPT findings
CPT domain findings: attention	Report all of the results related to attention if provided. These are typically reported as: d′/D-prime; omission errors, commission errors, Slow HRT, Inconsistent HRT SD, variability. Include exact numbers reported for each subgroup including mean and standard deviations. Do not provide your own interpretations of data.
CPT domain findings: sustained attention	Report all of the results related to sustained attention if provided: Hit Reaction Time/HRT Block Change; omissions by block; commissions by block. Include exact numbers reported for each subgroup including mean and standard deviations. Do not provide your own interpretations of data.
CPT domain findings: vigilance	Report all of the results related to vigilance if provided: HRT-ISI Change; Omissions by ISI; Commissions by ISI. Include exact numbers reported for each subgroup including mean and standard deviations. Do not provide your own interpretations of data.
CPT domain findings: impulsivity	Report all of the results related to impulsivity if provided: commission errors; perseveration scores; hit reaction times/HRT; HRT-Standard Deviation/HRT-SD. Include exact numbers reported for each subgroup including mean and standard deviations. Do not provide your own interpretations of data.
Findings related to other tests/measures (if applicable)	Narrative summary of the findings of the other tests/measures used
Conclusion
Study conclusion(s)	List the final conclusion(s) of the study
Comments	This is an other field for capturing internal notes, noting potential themes for manuscript, etc.

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) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

P. Douglas Callan

Dustin Shepler

Author Biographies

Dr. Callan is a licensed psychologist in Michigan. He is a core doctoral faculty member at the Michigan School of Psychology.

Ms. Swanberg, MSI, AHI is the user servies librarian and adjunct professor at Michigan School of Psychology.

Ms. Sarah Weber is a doctoral candidate at the Michigan School of Psychology.

Ms. Eidnes is a doctoral candidate at the Michigan School of Psychology.

Ms. Tara Pope is a doctoral candidate at the Michigan School of Psychology.

Dr Shepler is a licensed psychologist in Michigan. He is the Director of Training for the Michigan School of Psychology for both the doctoral and master’s prgrams.

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