Creativity in the Predominantly Inattentive and Combined Presentations of ADHD in Adults

Objective: ADHD and its associated inhibition deficits might promote creativity. However, results in the literature are conflicting, possibly due to the heterogeneity of ADHD. To control for this heterogeneity, creativity, and inhibition were investigated in the predominantly inattentive (ADHD-I) and combined (ADHD-C) presentations. Method: Participants were males/females aged 18 to 51, diagnosed with ADHD-I ( n = 21), ADHD-C ( n = 19), or without ADHD ( n = 43). Self-rated Kaufman Domains of Creativity Scale and evaluator-rated figural Torrance Test of Creative Thinking (TTCT) were used for measuring creativity, Stroop task for inhibition, and Conners’ Adult ADHD Rating Scales for ADHD symptoms. Results: The ADHD-C group reported higher self-rated creativity than other groups and made more original drawings paired to more abstract titles in the figural TTCT than controls. Conclusion: ADHD-C participants were the most creative. This result was more importantly associated with higher degrees of ADHD symptoms rather than poorer inhibition.

Non-credible symptom report in the clinical evaluation of adult ADHD: development and initial validation of a new validity index embedded in the Conners’ adult ADHD rating scales

As attention-deficit/hyperactivity disorder (ADHD) is a feasible target for individuals aiming to procure stimulant medication or accommodations, there is a high clinical need for accurate assessment of adult ADHD. Proven falsifiability of commonly used diagnostic instruments is therefore of concern. The present study aimed to develop a new, ADHD-specific infrequency index to aid the detection of non-credible self-report. Disorder-specific adaptations of four detection strategies were embedded into the Conners’ Adult ADHD Rating Scales (CAARS) and tested for infrequency among credible neurotypical controls (n = 1001) and credible adults with ADHD (n = 100). The new index’ ability to detect instructed simulators (n = 242) and non-credible adults with ADHD (n = 22) was subsequently examined using ROC analyses. Applying a conservative cut-off score, the new index identified 30% of participants instructed to simulate ADHD while retaining a specificity of 98%. Items assessing supposed symptoms of ADHD proved most useful in distinguishing genuine patients with ADHD from simulators, whereas inquiries into unusual symptom combinations produced a small effect. The CAARS Infrequency Index (CII) outperformed the new infrequency index in terms of sensitivity (46%), but not overall classification accuracy as determined in ROC analyses. Neither the new infrequency index nor the CII detected non-credible adults diagnosed with ADHD with adequate accuracy. In contrast, both infrequency indices showed high classification accuracy when used to detect symptom over-report. Findings support the new indices’ utility as an adjunct measure in uncovering feigned ADHD, while underscoring the need to differentiate general over-reporting from specific forms of feigning.

Use of machine learning to classify adult ADHD and other conditions based on the Conners’ Adult ADHD Rating Scales

A reliable diagnosis of adult Attention Deficit/Hyperactivity Disorder (ADHD) is challenging as many of the symptoms of ADHD resemble symptoms of other disorders. ADHD is associated with gambling disorder and obesity, showing overlaps of about 20% with each diagnosis. It is important for clinical practice to differentiate between conditions displaying similar symptoms via established diagnostic instruments. Applying the LightGBM algorithm in machine learning, we were able to differentiate subjects with ADHD, obesity, problematic gambling, and a control group using all 26 items of the Conners’ Adult ADHD Rating Scales (CAARS-S: S) with a global accuracy of .80; precision (positive predictive value) ranged between .78 (gambling) and .92 (obesity), recall (sensitivity) between .58 for obesity and .87 for ADHD. Models with the best 5 and best 10 items resulted in less satisfactory fits. The CAARS-S seems to be a promising instrument to be applied in clinical practice also for multiclassifying disorders displaying symptoms resembling ADHD.

Reliability, Validity, Factor Structure, and Measurement Invariance of the Japanese Conners’ Adult ADHD Rating Scales (CAARS)

To examine reliability, validity, factor structure, and measurement invariance (i.e., configural, metric, and scalar invariance) of the Japanese Conners’ Adult attention deficit hyperactivity disorder (ADHD) Rating Scales (CAARS), Japanese nonclinical adults ( N = 786) completed the CAARS Self-Report (CAARS-S). Each participant was also rated by one observer using the CAARS Observer Form (CAARS-O). For the test of measurement invariance, excerpts from the original (North American) CAARS norming data ( N = 500) were used. Dimensional structure was examined by exploratory and confirmatory factor analyses. Test–retest reliability, internal consistency, and concurrent validity were satisfactory. Based on the DSM-IV model and Japanese four-factor model, configural and metric invariance were established for the CAARS-S/O across Japanese and North American populations. Scalar invariance was established for the CAARS-O based on the Japanese model. The use of the Japanese CAARS for diagnostic purposes in Japan was supported; however, it should be used with caution for cross-cultural comparison research.

Malingered Attention Deficit/Hyperactivity Disorder on the Conners’ Adult ADHD Rating Scales: Do Reasons for Malingering Matter?

Malingering is a significant assessment concern in adults undergoing evaluations for attention deficit/hyperactivity disorder (ADHD) and may occur for a number of reasons, including access to medication and/or accommodations. Therefore, it is important to investigate ways to determine accuracy of self-reported ADHD symptoms. The present study used a simulation design to examine the impact of reasons for malingering on the Conners’ Adult ADHD Rating Scales (CAARS) Symptom subscales and the CAARS infrequency index (CII). Participants ( N = 157) were randomly assigned to one of three conditions: best effort, malingering for stimulant medication, or malingering for extra time accommodations. The three groups were compared with 34 individuals reporting previous diagnosis of ADHD. Results showed that individuals in both malingering groups scored higher than controls on all CAARS subscales and CII. Individuals in the medication malingering group, but not the extra time malingering group, scored higher than the ADHD group on CII and several CAARS subscales whose content overtly reflects ADHD symptomatology. Findings emphasize the influence of malingering on self-reported ADHD symptoms and the need to assess for malingering in ADHD evaluation. Results also suggest that reason for malingering might differentially affect self-report of ADHD symptoms.

Evaluating the Psychometric Properties of the Conners Adult ADHD Rating Scales

Objective: Characterized by significant work-related impairments, difficulties with relationships, and higher rates of criminal behavior and substance abuse, ADHD is now understood as a disability that can persist throughout a lifetime. Differing opinions remain, however, regarding adult symptoms of ADHD, particularly with regard to gender differences. These issues add to the challenges of constructing a reliable and valid measure of ADHD for use with adults in research and practice. Method: We review the theoretical foundations of ADHD and psychometric properties of a major assessment device, the Conners’ Adult ADHD Rating Scales (CAARS). Results: Results of the literature review found (a) a wide range of internal consistency estimates, with lowest values for the Diagnostic and Statistical Manual of Mental Disorders ( DSM)–Hyperactive/Impulsive Symptoms subscale when completed by women, and (b) discriminant validity estimates in the same range as convergent validity estimates. Conclusion: These findings raise questions about the construct validity of the CAARS and suggest directions for future research.