JOURNAL ARTICLE
Impact of Emotions on Test of Variables of Attention(TOVA) Performance in a Pediatric Clinical Population: A Retrospective Study.
Published In: Archives of Clinical Neuropsychology, 2023, v. 38, n. 7. P. 1047 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Barakat, Marc; Ezzeddine, Reem; Mohsen, Heba; Shamseddeen, Wael 3 of 3
Abstract
This article investigates the impact of parent-reported emotional symptoms, specifically anxiety and depression, on performance in the Test of Variables of Attention (TOVA) among a pediatric clinical population aged 8 to 18 years. Using retrospective data from 216 patients assessed at the American University of Beirut Medical Center, the study found no significant association between emotional symptoms measured by the Screen for Child Anxiety Related Disorders (SCARED) and Mood and Feelings Questionnaire (MFQ) and TOVA performance indices (response time variability, response time, commission errors, omission errors), even after controlling for sex and ADHD symptom severity. Additionally, changes in TOVA performance over time were not influenced by emotional symptom levels. The findings suggest that TOVA results are robust against reported negative emotional symptoms in youth, supporting its reliability for assessing attentional issues in clinical settings, though the authors recommend further research into other factors such as motor disabilities or sleepiness that might affect test outcomes.
Additional Information
- Source:Archives of Clinical Neuropsychology. 2023/10, Vol. 38, Issue 7, p1047
- Document Type:Article
- Subject Area:Health and Medicine
- Publication Date:2023
- ISSN:0887-6177
- DOI:10.1093/arclin/acad023
- Accession Number:174274769
- Copyright Statement:Copyright of Archives of Clinical Neuropsychology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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