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March 4, 2021
A Canadian team has published a systematic review examining the effectiveness of Mindfulness-Based Interventions (MBIs)for treating adults with ADHD. MBIs usually involves three forms of meditation –body scan, sitting meditation, and mindful yoga – that are intended to cultivate non-judgmental awareness of the present-moment experience. The team reviewed thirteen studies.
Three were single-group studies with no control group. One used dialectical behavior therapy (DBT). It reported mild to moderate improvements in ADHD symptoms, and substantial improvements in neurocognitive function (with standardized mean difference effect sizes from.99 to 2.22). A second enrolled both adults and adolescents in a mindful awareness program (MAP) which included a psychoeducational component. It found improvements in self-reported ADHD symptoms, with standardized mean difference(SMD) effect sizes running from .50 to.93. Following training, it also reported improvement in attentional conflict (.93) set-shifting (.43). The third study also used DBT, focused on acceptance, mindfulness, functional behavioral analysis, and psychoeducation. ADHD symptoms showed mild improvement (.22), and functional impairment was slightly reduced (.15) and remained stable at a 3-month follow-up.
The other ten studies used control groups. One used MAP and carefully stratified participants based on their ADHD medication status, then randomly assigned them to mindfulness treatment or waitlist. It reported large effect sizes in the improvement of self-reported and clinician ratings of ADHD symptoms (1.35 to 3.14), executive functioning (1.45 to 2.67), and self-reported emotion regulation (1.27 to 1.63). In another study, non randomly assigned adults to either mindfulness-based training (MBT) or skills training. Effect sizes were small to medium (.06 to .49), with 31% of MBT participants showing some improvement, versus only 11% of skills training participants.
Another study involved a controlled trial of college students with ADHD, randomized to receive either MBT or skills treatments. Treatment response rates were higher for MBT (59-65%, vs. 19-25%). At follow-up, the effect size for MBT on ADHD symptoms was large (.84), and similarly large on executive functioning (.81).
Another study tried a year’s worth of mindfulness training on poor responders to medication. Participants who received the treatment were compared to others who were waitlisted. The study reported a medium effect size (.63) in reducing the severity of ADHD.
Another looked at the impact of MAP on affective problems and impaired attention. It compared adults with ADHD and healthy controls who participated in MAP sessions with similar patients and controls who did not. The authors reported that MAP improved sustained attention and mood, with medium to large effect sizes (.50 to .80).
A recent study explored the impact of MAP on neurocognitive performance with a randomized controlled trial. Following an8-week mindfulness training, researchers “found a significant decrease in ADHD symptoms and significant improvement in task performance in both the MAP and the psychoeducation comparison group post - versus pre-intervention but did not find evidence for a significant main effect of treatment or a significant interaction effect on any ADHD symptoms (self-and observer-rated) nor on task performance (WM).”
Another study randomly assigned adults with ADHD either to a waitlist or to mindfulness-based cognitive therapy (MBCT). It found that MBCT led to a medium-to-large reduction in self-reported ADHD symptoms (.64) and a large reduction in investigator-reported symptoms (.78). It also found large (.93) improvements in executive functioning.
An 11th study looked at the effects of MBCT on neuropsychological correlates (event-related potentials(ERPs)) of performance monitoring in adults with ADHD. Half the patients were randomly assigned to MBCT, the other half to waitlist. MBCT produced reduced inattention, hyperactivity/impulsivity, and global ADHD index symptoms with medium to large effect sizes (.49 to .93).
A 12th study randomly assigned college students to MBCT or waitlist. At follow-up, participants who had received MBCT exhibited large (1.26) reductions in ADHD symptoms as well as greater treatment response rates (57%-71% vs. 23%-31%) versus waitlist. They also registered greater improvement on most neuropsychological performance and attentional scores.
Finally, another study compared the efficacy of MBCT plus treatment as usual (TAU) versus TAU only in reducing core symptoms in adults with ADHD. Participants were randomly assigned to an 8-weekly group therapy including meditation exercises, psycho education, and group discussions, or to TAU only, including pharmacotherapy and/or psycho education. At 6-month follow-up, MBCT+TAU patients reported large (SMD = .79) improvements in ADHD symptoms relative to TAU patients.
Overall, these are promising results for mindfulness-based interventions, and all the more so for those who do not respond well to drug therapy. Nevertheless, they must be seen as tentative. The sum total of participants over all thirten studies was just 753, or an average of only 58 per study. There was too much variation in the studies to perform a meta-analysis. Only one of the studies included a healthy (non-ADHD) control group. And only one study received a perfect sce by Cochrane Collaboration standards. Most studies did not use a suitable control group, i.e., on in which there was an expectation of benefit from participating. As the authors noted, “Attrition bias was found to have high or unclear risk in more than a half of the studies. The reason for dropout of participants was not always clearly specified in those studies, so it is difficult to decide if it might be related to adverse effects or to some discomfort with treatment or instead to some incidental reasons.”
Hélène Poissant, Adrianna Mendrek, Nadine Talbot, Bassam Khoury, and Jennifer Nolan, “Behavioral and Cognitive Impacts of Mindfulness-Based Interventions on Adults with Attention-Deficit Hyperactivity Disorder: A Systematic Review,” Behavioural Neurology, Vol. 2019, Article ID 5682050, 16 pages, https://doi.org/10.1155/2019/5682050.
For centuries, we’ve called the eyes the "windows to the soul," but for modern neurologists, they are quite literally a window into the brain. The retina and the central nervous system share the same embryonic origins, developing from the same neural tissue in the womb. Because of this deep biological connection, the back of your eye acts as a non-invasive map of your brain's health, displaying a complex web of nerves and blood vessels that can (theoretically!) mirror certain neurodevelopmental conditions.
Recently, a buzz rippled through the mental health community when a study published in partnership with Seoul National University Bundang Hospital claimed a massive breakthrough. Researchers developed an Artificial Intelligence (AI) model that could screen children for Attention-Deficit/Hyperactivity Disorder (ADHD) using nothing more than a simple retinal photograph. The study, which prospectively recruited children from Severance Hospital and Eunpyeong St. Mary’s Hospital, produced results that were staggering: the AI reportedly achieved an accuracy rate of 96.9%!
In the world of medical testing, scientists use a metric called AUROC (Area Under the Receiver Operating Characteristic) to measure how well a test works.
An AUROC of 96.9% is a near-perfect score, suggesting a tool is ready for immediate, real-world deployment. While headlines promised a revolution in mental health screening, a deeper look into this research and the study’s design has exposed that this 96.9% AUROC was more likely evidence of a flawed methodology rather than a biological reality.
To build their screening tool, researchers analyzed over 1,100 retinal images using a digital pipeline called AutoMorph and a machine-learning model known as XGBoost. The AI was trained to hunt for physical signals of the "Dopamine Connection." Dopamine is the primary neurotransmitter involved in ADHD, but it is also essential to the eye. It regulates synaptic formation, retinal blood flow, and vascular endothelial regulation. Because dopamine dysregulation influences how blood vessels grow and remodel, the study hypothesized that an ADHD brain would leave a unique "fingerprint" on the retinal vasculature, resulting in denser, thicker vessel structures.
On paper, the logic was sound: use AI to spot the subtle vascular remodeling caused by dopaminergic shifts. But a closer look at the investigation revealed that the AI wasn't just spotting ADHD; it was over-indexing on technical noise.
The most significant "smoking gun" flagged by critics is a massive temporal mismatch. In other words, there was a severe disparity in the timeframes and conditions under which the retinal images for the two comparison groups were collected. For an AI to learn a biological condition, it must compare groups under identical technical conditions. Instead, this study created a time-traveling dataset:
A scientific study is only as reliable as its control group. The control in any experiment acts as a baseline against which the study group is compared. In this case, the control group should be composed of children without any neurodevelopmental disorders, or of “typically developing” children.
In this study, the control group wasn't composed of healthy children from the community. Instead, they were patients visiting a tertiary ophthalmology clinic. Children visiting a specialist eye hospital are rarely "typical." They are there because they have symptomatic eye issues. This introduced a massive selection bias involving three major confounders:
When training AI, you must never allow the "test questions" to leak into the "study material." The researchers, however, committed a fundamental violation of machine learning hygiene known as Eye-to-Eye Data Leakage. The study split the data by the eye rather than by the participant.
Human eyes are highly correlated; the left eye is a near-mirror of the right. If a child's left eye was used for training and their right eye was used for testing, the AI was effectively "cheating." Instead of learning the general traits of ADHD, the model was potentially memorizing individuals. This error artificially balloons accuracy metrics.
The true test of medical AI is diagnostic specificity, or differential diagnosis. This refers to the ability to tell one condition apart from another. While the model claimed 96.9% accuracy against a flawed control group, its performance collapsed when faced with real-world complexity.
When the researchers asked the AI to differentiate between ADHD and Autism Spectrum Disorder (ASD), the accuracy plummeted to a poor 63% AUROC. In real-world clinical settings, an accuracy of 63% is dangerously close to a 50% coin flip. Since ADHD frequently co-occurs with ASD, anxiety, or intellectual disabilities, an AI that cannot handle these "clinical differentials" is functionally useless in a doctor's office. The failure at this stage proves the model was likely detecting technical quirks of the dataset rather than a unique biological marker for ADHD.
To move from the lab to the clinic, we must establish a foundation built on rigor rather than high-speed data scraping. Moving forward, we must demand these 3 Pillars of Trusted Medical AI :
The dream of a quick eye scan to diagnose ADHD is not dead, but it must be rescued from "fast science" shortcuts and buzzy headlines.
Background:
One of the more persistent concerns among parents of children with ADHD is whether stimulant medications will stunt their child's growth. A large Israeli cohort study now offers some of the most rigorous reassurance to date, and its methodology sets it apart from earlier research.
The question has long been complicated by a more fundamental uncertainty: do growth differences in children with ADHD stem from the condition itself, from stimulant treatment, or from factors present before any medication is ever prescribed? Without a clear answer, clinicians and families have faced a genuine dilemma when weighing the benefits of stimulant therapy against potential long-term physical costs.
Most previous studies compounded this difficulty by comparing group-average heights, which ignores the crucial variable of genetic potential. A child who is short relative to the general population may simply have short parents. Failing to account for this introduces systematic bias and can make medications appear more harmful than they are.
The Study:
The Israeli research team addressed this directly. Using health records from a nationwide provider, they assembled a retrospective cohort of children born between 1995 and 2003, following them through 2023. This amount of time was long enough for all participants to have reached adult stature (defined as 17 or older for females, 19 or older for males). Their sample included 5,671 children with untreated ADHD, 11,846 who received stimulant treatment, and 47,258 non-ADHD controls. Children who took stimulants for only one to two months, or who had chronic medical conditions requiring long-term medication, were excluded to avoid confounding the results.
Crucially, adult height was evaluated not against population norms but against each individual's expected height, calculated from parental heights using the Tanner-Goldstein-Whitehouse method, a standard approach for estimating genetic height potential via mid-parental height.
When the researchers compared adult heights across the three groups using analysis of variance (ANOVA), they did find statistically significant differences. But statistical significance, particularly in studies with tens of thousands of participants, does not automatically translate into clinical significance. The effect sizes were consistently very small, and the absolute differences were under one centimeter, which is a margin considered clinically negligible.
Their conclusion is measured but clear: after accounting for genetic growth potential, neither an ADHD diagnosis nor stimulant treatment was associated with meaningful reductions in adult height. The findings, they argue, support prioritizing behavioral and functional outcomes when making treatment decisions, since the risk of clinically significant height loss appears to be minimal.
The Take-Away:
For families navigating ADHD treatment, the practical implication is significant: concerns about permanent growth suppression, while understandable, should not be the primary driver of whether or how long a child receives stimulant therapy.
A recent meta-analysis examined how well cognitive behavioral therapy (CBT) improves not just symptoms, but everyday functioning and quality of life in adults with ADHD.
The Background:
ADHD in adults affects far more than attention or impulsivity. It often disrupts key areas of life:
These broad impacts highlight a key issue: reducing symptoms does not automatically translate into better day-to-day functioning.
CBT is a structured, skills-based therapy that helps people:
While both medication (especially stimulants) and CBT improve core ADHD symptoms, CBT is particularly aimed at improving real-world functioning.
The Study:
The researchers analyzed studies involving adults diagnosed with ADHD (or showing clinically significant symptoms). They included:
They focused specifically on outcomes beyond symptoms:
The Results:
1. Strongest Effects: Occupational functioning
CBT showed consistently strong improvements in work-related functioning compared to control groups, both immediately after treatment and at follow-up. This was the most robust finding across domains.
2. Moderate Improvement: Global Functional Impairment
CBT led to moderate improvements in overall daily functioning, with some evidence that gains persist over time. In studies tracking individuals over time, improvements were even stronger at follow-up.
3. Modest Gains: Social Relationships
CBT produced small to moderate improvements in social functioning. Benefits were present both after treatment and at follow-up, but were less pronounced than in work-related outcomes.
4. Limited Effects: Academic Functioning
There were moderate short-term gains when CBT was compared to control groups, but these did not persist at follow-up. Within-subject studies showed only small improvements overall.
5. Modest and Inconsistent Effects: Quality of Life
Improvements in quality of life were small when compared to control groups and often did not last. However, studies tracking individuals over time showed moderate improvements, suggesting some benefit that may not always show up clearly in between-group comparisons.
Overall, the findings suggest:
One notable nuance: CBT did not always outperform other active treatments (like medication or other therapies). This suggests that while CBT is effective, its benefits may partly overlap with broader therapeutic or support effects rather than relying on a single, unique mechanism.
The Take-Away:
CBT is a valuable, evidence-based treatment for adults with ADHD, especially for improving work functioning and overall daily life management. However, its impact on relationships, academic outcomes, and quality of life is more limited and less consistent, pointing to the need for more targeted or combined approaches in those areas.
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