Assessing ADHD symptoms in children and adults: Evaluating the role of objective measures

Theresa S. Emser, Blair Johnston, John Steele, Sandra Kooij, Lisa Thorell, Hanna Christiansen

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    40 Citations (Scopus)
    524 Downloads (Pure)


    Background: Diagnostic guidelines recommend using a variety of methods to assess and diagnose ADHD. Applying subjective measures always incorporates risks such as informant biases or large differences between ratings obtained from diverse sources. Furthermore, it has been demonstrated that ratings and tests seem to assess somewhat different constructs. The use of objective measures might thus yield valuable information for diagnosing ADHD. This study aims at evaluating the role of objective measures when trying to distinguish between individuals with ADHD and controls. Our sample consisted of children (n = 60) and adults (n = 76) diagnosed with ADHD and matched controls who completed self- and observer ratings as well as objective tasks. Diagnosis was primarily based on clinical interviews. A popular pattern recognition approach, support vector machines, was used to predict the diagnosis.

    Results: We observed relatively high accuracy of 79% (adults) and 78% (children) applying solely objective measures. Predicting an ADHD diagnosis using both subjective and objective measures exceeded the accuracy of objective measures for both adults (89.5%) and children (86.7%), with the subjective variables proving to be the most relevant.

    Conclusions: Considering the high accuracy of objective measures only and the fact that subjective measures are subject to influence by rater bias, we argue that using objective measures may be more replicable to aid diagnosis and should be incorporated in diagnostic procedures for assessing ADHD.
    Original languageEnglish
    Article number11
    Pages (from-to)1-14
    Number of pages14
    JournalBehavioral and Brain Functions
    Early online date18 May 2018
    Publication statusPublished - 18 May 2018


    • ADHD
    • Children/adults
    • Classification
    • Objective assessment
    • Support vector machines

    ASJC Scopus subject areas

    • Cognitive Neuroscience
    • Biological Psychiatry
    • Behavioral Neuroscience


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