Assessing the Contribution of Measures of Attention and Executive Function to Diagnosis of ADHD or Autism
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| Title: | Assessing the Contribution of Measures of Attention and Executive Function to Diagnosis of ADHD or Autism |
|---|---|
| Language: | English |
| Authors: | Kelsey Harkness (ORCID |
| Source: | Journal of Autism and Developmental Disorders. 2025 55(4):1353-1364. |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 12 |
| Publication Date: | 2025 |
| Document Type: | Journal Articles Reports - Research |
| Descriptors: | Adolescents, Autism Spectrum Disorders, Attention Deficit Hyperactivity Disorder, Executive Function, Attention Control, Diagnostic Tests, Disability Identification, Cognitive Tests, Symptoms (Individual Disorders), Error of Measurement, Measurement Techniques, Comparative Testing, Evaluation Criteria, Evaluation Methods, Evaluation Problems |
| DOI: | 10.1007/s10803-024-06275-9 |
| ISSN: | 0162-3257 1573-3432 |
| Abstract: | Attention and executive function (EF) dysregulation are common in a number of disorders including autism and attention-deficit/hyperactivity disorder (ADHD). Better understanding of the relationship between indirect and direct measures of attention and EF and common neurodevelopmental diagnoses may contribute to more efficient and effective diagnostic assessment in childhood. We obtained cognitive (NIH Toolbox, Little Man Task, Matrix Reasoning Task, and Rey Delayed Recall) and symptom (CBCL, and BPMT) assessment data from the Adolescent Brain and Cognitive Development (ABCD) database for three groups, autistic (N = 110), ADHD (N = 878), and control without autism or ADHD diagnoses (N = 9130) and used ridge regression to determine which attention and EF assessments were most strongly associated with autism or ADHD. More variance was accounted for in the model for the ADHD group (31%) compared to the autism group (2.7%). Finally, we ran odds ratios (using clinical cutoffs where available and 2 standard deviations below the mean when not) for each assessment measure, which generally demonstrated a greater significance within the indirect measures when compared to the direct measures. These results add to the growing literature of symptom variably across diagnostic groups allowing for better understanding of presentations in autism and ADHD and how best to assess diagnosis. It also highlights the increased difficulty in differentiating autism and controls when compared to ADHD and controls and the importance of indirect measures of attention and EF in this differentiation. |
| Abstractor: | As Provided |
| Entry Date: | 2025 |
| Accession Number: | EJ1464204 |
| Database: | ERIC |
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