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Child Neuropsychology
A Journal on Normal and Abnormal Development in Childhood and Adolescence
Volume 23, 2017 - Issue 2
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Original Articles

Relating lab to life: Decrements in attention over time predict math productivity among children with ADHD

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Pages 148-158 | Received 07 May 2015, Accepted 31 Aug 2015, Published online: 23 Sep 2015
 

Abstract

A child’s ability to sustain attention over time (AOT) is critical in attention-deficit/hyperactivity disorder (ADHD), yet no prior work has examined the extent to which a child’s decrement in AOT on laboratory tasks relates to clinically-relevant behavior. The goal of this study is to provide initial evidence for the criterion validity of laboratory assessments of AOT. A total of 20 children with ADHD (7–12 years of age) who were enrolled in a summer treatment program completed two lab attention tasks (a continuous performance task and a self-paced choice discrimination task) and math seatwork. Analyses focused on relations between attention task parameters and math productivity. Individual differences in overall attention (OA) measures (averaged across time) accounted for 23% of the variance in math productivity, supporting the criterion validity of lab measures of attention. The criterion validity was enhanced by consideration of changes in AOT. Performance on all laboratory attention measures deteriorated as time-on-task increased, and individual differences in the decrement in AOT accounted for 40% of the variance in math productivity. The only variable to uniquely predict math productivity was from the self-paced choice discrimination task. This study suggests that attention tasks in the lab do predict a clinically-relevant target behavior in children with ADHD, supporting their use as a means to study attention processes in a controlled environment. Furthermore, this prediction is improved when attention is examined as a function of time-on-task and when the attentional demands are consistent between lab and life contexts.

We thank Mark Kutgowski for computer programming, Doug Medina for assistance with data collection, and Craig Colder and Nicolas Schlienz for their feedback during preparation of this manuscript.

No potential conflict of interest was reported by the authors.

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