ADHD: Software supports diagnostic process

To be diagnosed with ADHD, children must exhibit various factors and persistent patterns of inattention, hyperactivity, and impulsivity. It can sometimes be difficult to diagnose the condition because younger children change and develop rapidly. New software makes it possible to combine and analyze different evaluations and test results to facilitate a more informed and accurate diagnosis.

In this MEDICA-tradefair.com interview, Professor Wolfgang Lenhard explains how the software works and describes why this approach differs from existing diagnostic methods.

Professor Lenhard, what prompted this software development?

Professor Wolfgang Lenhard: Attention-deficit/hyperactivity disorder (ADHD) is a very complex, multi-layered condition, which presents diagnostic challenges. We have been studying various approaches to improve ADHD diagnostic assessments since 2007. Back then, you had new basic research findings on the differences between children with and without ADHD - specifically related to the differences in reaction time pertaining to central-executive functioning. From there we carried out several preliminary studies and obtained project financing. For an accurate diagnosis, the children's symptoms must occur persistently across different areas of life and be subsequently incorporated in a strategic diagnosis that merges all sources of information. The question here is how to weight and consolidate different aspects and how to address inconsistent findings. Our goal here is to reduce misdiagnoses and to give the diagnosed persons more certainty, thus ensuring that children receive more successful treatment and target outcomes.

How does the software work?

Lenhard: The software processes diagnostic results and creates a report. It also involves a computer-based test, which is one of the sources of information for the entire diagnostic process. This includes the assessment of executive functions - in this specific case it pertains to so-called inhibition or inhibitory control. This tests whether the children can block out disturbing stimuli and exhibit impulse control. This is mapped via an Eriksen flanker task as part of a test in the software. It consists of many arrows appearing on the screen. The child must identify and respond to a single arrow in the middle that appears about 100 milliseconds later. There is also a so-called Go-No-Go task. Geometric shapes appear and the child must press the spacebar as fast as possible on certain shapes, except for one shape where the child must suppress the response. The child’s correct answers and distributions for response times for the individual tasks are measured within this framework. Besides the mean value and error rates, variance, skewness of the reaction times and other distribution parameters are also incorporated.

This obtained information is combined and results in a function to distinguish between children with and without ADHD. Along with this objective performance measurement, the diagnostic process also incorporates subjective aspects, which includes the assessments of parents and teachers. All three sources of information are brought together in the software to create an overall assessment. It was important to us that the software is easy to use and ensures that data is secure. It doesn’t require an internet connection and information can stay within the physician’s practice.

What are the advantages of the software over the existing methods to diagnose ADHD in children?

Lenhard: The process draws on established approaches. The benefit of this process is that we link the available information in a new way. All sources of information have undergone continuous normalization, which means that the age effect is controlled. The normative tables are accurate to the week unlike many other methods that are based on annual norms. Children and their performance develop very quickly, which makes this a better way to record progress. The multiple sources of information - objective performance measurement, parental and teacher assessments - make it easier to track whether the results are consistent, whether something is inconsistent and to determine which ones are valid in the respective cases. These comparisons are backed up by psychometric testing, ensuring the reliability of the results. This allows us to get a balanced representation of ADHD diagnosis in children.

Where do you see room for improvement when it comes to diagnosing mental disorders in children?

Lenhard: Children’s mental health has generally become a hot topic. The COVID-19 pandemic has pushed many children into a difficult situation, which was often not recognized until it was too late. Problems were frequently only noticed when they had already become clinically relevant or progressed to a point where urgent action was required. This makes prevention and early detection of adverse developments very important. Needless to say, it’s difficult to start early intervention, but an early diagnosis can make all the difference in the world for children. Meanwhile, diagnostic methodology is an ever-evolving area. Examples of this include how process data is utilized and how subjective questionnaires are no longer the main focus. Normalization is another key point as it enables you to get accurate comparison results. We need more representative and larger samples with a good methodology in this setting.