February 18, 2021

Can College Students Trying to Fake ADHD be Detected?

Many college students truly have ADHD and deserve to be treated but some attempt to fake ADHD symptoms with the goal of getting stimulant medications for non-medical uses such as studying and getting high.  Some students who fake ADHD also seek to gain accommodations that would give them additional time to complete exams. To address this issue, two psychologists examined data from 514 university students being assessed for ADHD to evaluate the ability of assessment tools to detect students who fake ADHD symptoms.

All participants had asked to be assessed to determine whether they could qualify for disability services. This was therefore by no means a random sample of university students, and could be expected to include some non-ADHD individuals seeking the benefits of an ADHD diagnosis.; however, this offered a good opportunity to explore which combination of tools would yield the best accuracy, and be best at excluding malingerers.

That was achieved by using both multiple informants and multiple assessment tools, and comparing results. Self-assessment was supplemented by assessment by other informants (e.g. parent, partner, friend, or other relative). These were supplemented with symptom validity tests to check for telltale highly inconsistent symptom reporting, or symptom exaggeration, which could signal false positives.

On the other hand, some individuals with ADHD have executive functioning problems that may make it difficult for them to reliably appraise their own symptoms on self-assessment tests, which can lead to false negatives. Performance validity tests were therefore also administered, in order to detect poor effort during evaluation, which could lead to false negatives.

Observer reporting was found to be more reliable than self-reporting, with significantly lower inconsistency scores (p< .001), and significantly higher exaggeration scores (p < .001). More than twice as many self-reports showed evidence of symptom exaggeration as did observer reports. This probably understates the problem when one considers that the observer reports were performed not by clinicians but by parents and partners who may themselves have had reasons to game the tests in favor of an ADHD diagnosis.

Even so, the authors noted, “External incentives such as procurement of a desired controlled substance or eligibility for a desired disability accommodation are likely to be of more perceived value to those who directly obtain them.” They suggested compensating for this by making ADHD diagnoses only on the basis of positive observer tests in addition to self-reports: “Applying an ‘and’ rule—one where both self- and observer reports were required to meet the diagnostic threshold— generally cut the proportions meeting various thresholds at least in half and washed out the differences between the adequate and inadequate symptom validity groups.”

They also recommended including formal tests of response validity, using both symptom validity tests and performance validity tests. Overall, they found that just over half the subsample of 410 students administered performance validity tests demonstrated either inadequate symptom or performance validity.

Finally, they recommended “that clinicians give considerable weight to direct, objective evidence of functional impairment when making decisions about the presence of ADHD in adults. The degree to which symptoms cause significant difficulty functioning in day-to-day life is a core element of the ADHD diagnostic criteria (American Psychiatric Association,2013), and it cannot be assumed that significant symptoms cause such difficulty, as symptoms are only moderately associated with functional impairment. we urge clinicians to procure objective records (e.g., grade transcripts, work performance evaluations, disciplinary and legal records) to aid in determining functional impairment in adults assessed for ADHD.”

Jason M. Nelson and Benjamin J. Lovett, “Assessing ADHD in College Students: Integrating Multiple Evidence Sources With Symptom and Performance Validity Data,” Psychological Assessment, published online January 31, 2019 http://dx.doi.org/10.1037/pas0000702.

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Using Video Analysis and Machine Learning in ADHD Diagnosis

NEWS TUESDAY: Machine Learning and The Possible Future of Diagnosing ADHD

Typically, clinicians rely on both subjective and objective observations, patient interviews and questionnaires, as well as reports from family and (in the case of children) parents and teachers, in order to diagnose ADHD. 

A group of researchers are aiming to find a diagnostic test that is purely objective and utilizes recent technological advancements. The method they developed involves analyzing videos of children in outpatient settings, focusing on their movements. The study included 96 children, half of whom had ADHD and half who did not.

How It Works

  1. Video Recording: Children were recorded during their outpatient visits.
  2. Skeleton Detection: Using a tool called OpenPose, the researchers detected and tracked the children's skeletons (essentially a map of their body's movements) in the videos.
  3. Movement Analysis: The researchers analyzed these movements, looking at 11 different movement features. They specifically focused on the angles of different body parts and how much they moved.
  4. Machine Learning: Six different machine learning models were used to see which movement features could best distinguish between children with ADHD and those without.

Key Findings

  • Movement Differences: Children with ADHD showed significantly more movement in all the features analyzed compared to children without ADHD.
  • Thigh Angle: The angle of the thigh was the most telling feature. On average, children with ADHD had a thigh angle of about 157.89 degrees, while those without ADHD had an angle of 15.37 degrees.
  • High Accuracy: Using thigh angle alone, the model could diagnose ADHD with 91.03% accuracy. It was very sensitive (90.25%) and specific (91.86%), meaning it correctly identified most children with ADHD and correctly recognized most children without it.

This new method could potentially provide a more objective way to diagnose ADHD, reducing the reliance on subjective observations and reports. It can help doctors make more accurate diagnoses, ensuring that those who need help get it and that those who don't aren't misdiagnosed.

May 28, 2024

Understanding Attention to Social Images in Children with ADHD and Autism

NEWS TUESDAY: Understanding Attention to Social Images in Children with ADHD and Autism

In the field of mental health, professionals often use a variety of tools to diagnose and understand neurodevelopmental disorders such as Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD). One such tool is the Autism Diagnostic Observation Schedule (ADOS), which is specifically designed to help diagnose autism. However, the ADOS wasn't originally intended for children who have both autism and ADHD, though this comorbidity is not uncommon.

A recent study aimed to explore how children with ADHD, autism, or both, pay attention to social images, such as faces. The study focused on using eye-tracking technology to measure where children direct their gaze when viewing pictures, and how long they look at certain parts of the image. This is important because differences in visual attention can provide insights into the nature of these disorders.

The researchers included 84 children in their study, categorized into four groups: those with ASD, those with ADHD, those with both ASD and ADHD, and neurotypical (NT) children without these conditions. During the study, children were shown social scenes from the ADOS, and their eye movements were recorded. The ADOS assessment was administered afterward. To ensure that the results were not influenced by medications, children who were on stimulant medications for ADHD were asked to pause their medication temporarily.

The results of the study showed that children with ASD, whether they also had ADHD or not, tended to spend less time looking at faces compared to children with just ADHD or NT children. The severity of autism symptoms, measured by the Social Communication Questionnaire (SCQ), was associated with reduced attention to faces. Interestingly, ADHD symptom severity, measured by Conners' Rating Scales (CRS-3), did not correlate with how children looked at faces.

These findings suggest that measuring visual attention might be a valuable addition to the assessment process for ASD, especially in cases where ADHD is also present. The study indicates that if a child with ADHD shows reduced attention to faces, it might point to additional challenges related to autism. The researchers noted that more studies with larger groups of children are needed to confirm these findings, but the results are promising. They hope that such measures could eventually enhance diagnostic processes and help in managing the complexities of cases involving comorbidity of ADHD and ASD.

This research opens up the possibility of using eye-tracking as a supplementary diagnostic tool in the assessment of autism, providing a more nuanced understanding of how attentional differences in social settings are linked to ASD and ADHD.

May 14, 2024

NEW STUDY: RASopathies Influences on Neuroanatomical Variation in Children

NEW STUDY: RASopathies Influences on Neuroanatomical Variation in Children

This study investigates how certain genetic disorders, called RASopathies, affect the structure of the brain in children. RASopathies are conditions caused by mutations in a specific signaling pathway in the body. Two common RASopathies are Noonan syndrome (NS) and neurofibromatosis type 1 (NF1), both of which are linked to a higher risk of autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD).

The researchers analyzed brain scans of children with RASopathies (91 participants) and compared them to typically developing children (74 participants). They focused on three aspects of brain structure: surface area (SA), cortical thickness (CT), and subcortical volumes.

The results showed that children with RASopathies had both similarities and differences in their brain structure compared to typically developing children. They had increased SA in certain areas of the brain, like the precentral gyrus, but decreased SA in other regions, such as the occipital regions. Additionally, they had thinner CT in the precentral gyrus. However, the effects on subcortical volumes varied between the two RASopathies: children with NS had decreased volumes in certain structures like the striatum and thalamus, while children with NF1 had increased volumes in areas like the hippocampus, amygdala, and thalamus.

Overall, this study highlights how RASopathies can impact the development of the brain in children. The shared effects on SA and CT suggest a common influence of RASopathies on brain development, which could be important for developing targeted treatments in the future.

In summary, understanding how these genetic disorders affect the brain's structure can help researchers and healthcare professionals develop better treatments for affected children.

April 30, 2024