September 4, 2021

How do undiagnosed but symptomatic adults compare with those diagnosed with ADHD?

The study team began with a representative sample of 69,972U. S. adults aged 18 years or older who completed the 2012 and 2013 U.S. National Health and Wellness Survey. These adults were invited to complete the Validate Attitudes and Lifestyle Issues in Depression, ADHD, and Troubles with Eating(VALIDATE) study, which included 1) a customized questionnaire designed to collect data on sociodemographic and clinical characteristics and lifestyle, and2) several validated work productivity, daily functioning, self-esteem, and health-related quality of life (HRQoL) questionnaires. Of the 22,937 respondents, 444 had been previously diagnosed with ADHD, and 1,055 reported ADHD-like symptoms but had no previous clinical diagnosis.

There were no significant differences between the two groups in terms of age, education, income, health insurance, and most comorbid disorders. But those who had not been previously diagnosed were significantly more likely to be first-generation Americans (p<.001), nonwhite (p<.001), unemployed (p=.024), or suffer from depression, insomnia, or hypertension.

After matching the two groups for sociodemographic characteristics and comorbid conditions, covariate comparisons were made between 436 respondents diagnosed with ADHD and 867 previously undiagnosed respondents. Among respondents who were employed, diagnosed individuals registered a mean work productivity loss of 29% as opposed to 49% for the previously undiagnosed (p<.001). They also registered a 37% level of activity impairment versus a 53% level among the undiagnosed(p<.001). On the Sheehan Disability Scale, which ranges from 0 (no impairment) to 30 (highly impaired), the diagnosed group had a mean of 10, as opposed to a mean of 15 for the undiagnosed (p<.001). Diagnosed respondents also significantly outperformed undiagnosed ones on the Rosenberg Self-Esteem Scale (19 versus 15, on a scale of 0 to 30, p<.001), and on two quality-of-life scales (p<.001).

Applying a linear regression mixed model to the matched sets, the diagnosed still scored 16 points better than the undiagnosed on the WPA I: GH Productivity Loss scale (p<.001), 14 points better on the WPA I: GH Activity Impairment scale (p<.001), 4.5 points better on the Sheehan Disability Scale(p<.001), almost 4 points on the Rosenberg Self-Esteem Scale (p<.0001), with comparable gains on the two quality-of-life scales (p<.001 and p<.0001).

The authors concluded, This comparison revealed that individuals who had been diagnosed with ADHD were more likely to experience better functioning, HRQoL [health related quality-of-life], and self-esteem than those with symptomatic ADHD. This result appears to be robust, withstanding several levels of increasingly rigorous statistical adjustment. That points to substantial benefits from the treatment that follows a diagnosis of adult ADHD.

Manjiri Pawaskar, Moshe Fridman, Regina Grebla, and ManishaMadhoo, "Comparison of Quality of Life, Productivity, Functioning and self-Esteem in Adults Diagnosed With ADHD and With Symptomatic ADH," Journal of Attention Disorders, Published online May 2, 2019, https://doi.org/10.1177/1087054719841129.

Related posts

No items found.

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

News Tuesday: Integrating Cognition and Eye Movement

Integrating Cognitive Factors and Eye Movement Data in Reading Predictive Models for Children with Dyslexia and ADHD-I

In a recent study, researchers delved into the complex interplay of cognitive processes and eye movements in children with dyslexia and Attention-Deficit/Hyperactivity Disorder. Their findings shed light on predictive models for reading outcomes in these children compared to typical readers.

The study involved 59 children: 19 typical readers, 21 with ADHD, and 19 with developmental dyslexia (DD), all in the 4th grade and around 9 years old on average. Each group underwent thorough neuropsychological and linguistic assessments to understand their psycholinguistic profiles.

During the study, participants engaged in a silent reading task where the text underwent lexical manipulation. Researchers then analyzed eye movement data alongside cognitive factors like memory, attention, and visual processes.

Using multinomial logistic regression, the researchers evaluated predictive models based on three key measures: a linguistic model focusing on phonological awareness, rapid naming, and reading fluency; a cognitive neuropsychological model incorporating memory, attention, and visual processes; and an additive model combining lexical word properties with eye-tracking data, specifically examining word frequency and length effects.

By integrating eye movement data with cognitive factors, the researchers enhanced their ability to predict the development of dyslexia or ADHD, in comparison to typically developing readers. This approach significantly improved the accuracy of predicting reading outcomes in children with learning disabilities.

These findings have profound implications for understanding and addressing reading challenges in children. By considering both cognitive processes and eye movement patterns, educators and clinicians can develop more effective interventions tailored to the specific needs of children with dyslexia and ADHD.

April 30, 2024