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June 22, 2021
Several meta-analyses have assessed this question by computing the Standardized Mean Difference or SMD statistic. The SMD is a measure that allows us to compare different studies. For context, the effect of stimulant medication for treating ADHD is about 0.9. SMDs less than 0.3 are considered low, between 0.3 to 0.6 medium, and anything greater than high.
A 2004 meta-analysis combined the results of fifteen studies with a total of 219 participants and found a small association(SMD = .28, 95% CI .08-.49) between consumption of artificial food colors by children and increased hyperactivity. Excluding the smallest and lowest quality studies further reduced the SMD to .21, and a lower confidence limit of .007 also made it barely statistically significant. Publication bias was indicated by an asymmetric funnel plot. No effort was made to correct the bias.
A 2012 meta-analysis by Nigg et al. combined twenty studies with a total of 794 participants and again found a small effect size (SMD =.18, 95% CI .08-.29). It likewise found evidence of publication bias. Correcting for the bias led to a tiny effect size at the outer margin of statistical significance (SMD = .12, 95% CI .01-.23). Restricting the pool to eleven high-quality studies with 619 participants led to a similarly tiny effect size that fell just outside the 95% confidence interval (SMD = .13, CI =0-.25, p = .053). The authors concluded, "Overall, a mixed conclusion must be drawn. Although the evidence is too weak to justify action recommendations absent a strong precautionary stance, it is too substantial to dismiss."
In 2013 a European ADHD Guidelines Group consisting of 21 researchers (Sonuga-Barke et al.) performed a systematic review and meta-analysis that examined the efficacy of excluding artificial colors from the diets of children and adolescents as a treatment for ADHD. While many interventions showed benefits in unblinded assessments, only artificial food color exclusion and, to a lesser extent, free fatty acid supplementation remained effective under blinded conditions. The findings suggest that eliminating artificial food dyes may meaningfully reduce ADHD symptoms in some children, though it should be noted that the positive results were mostly seen in children with other food sensitivities.
The research to date does suggest a small effect of artificial food colors in aggravating symptoms of hyperactivity in children, and a potential beneficial effect of excluding these substances from the diets of children and adolescents, but the evidence is not very robust. More studies with greater numbers of participants, and better control for the effects of ADHD medications, will be required for a more definitive finding.
In the meantime, given that artificial food colors are not an essential part of the diet, parents could consider excluding them from their children's meals, since doing so is risk-free, and the cost (reading labels) is negligible.
Joel T. Nigg, Kara Lewis, Tracy Edinger, Michael Falk, “Meta-Analysis of Attention-Deficit/Hyperactivity Disorder attention-Deficit/Hyperactivity Disorder Symptoms, Restriction Diet, and synthetic Food Color Additives,” Journal of The American Academy of Child & Adolescent Psychiatry (2012), Vol.51, No. 1, 86-97.David W. Schab and Nhi-Ha T. Trinh, “Do Artificial FoodColors Promote Hyperactivity in Children with Hyperactive Syndromes? Aneta-Analysis of Double-Blind Placebo-Controlled Trials,” Developmental and behavioral Pediatrics(2004), Vol. 25, No. 6, 423-434.Edmund J.S. Sonuga-Barke et al., “NonpharmacologicalInterventions for ADHD: Systematic Review and Meta-Analyses of RandomizedControlled Trials of Dietary and Psychological Treatments,” American Journal of Psychiatry(2013), 170:275-289.
If we are to read what we believe on the Internet, dieting can cure many of the ills faced by humans. Much of what is written is true. Changes in dieting can be good for heart disease, diabetes, high blood pressure, and kidney stones to name just a few examples. But what about ADHD? Food elimination diets have been extensively studied for their ability to treat ADHD. They are based on the very reasonable idea that allergies or toxic reactions to foods can have effects on the brain and could lead to ADHD symptoms.
Although the idea is reasonable, it is not such an easy task to figure out what foods might cause allergic reactions that could lead to ADHD symptoms. Some proponents of elimination diets have proposed eliminating a single food, others include multiple foods, and some go as far as to allow only a few foods to be eaten to avoid all potential allergies. Most readers will wonder if such restrictive diets, even if they did work, are feasible. That is certainly a concern for very restrictive diets.
Perhaps the most well-known ADHD diet is the Feingold diet(named after its creator). This diet eliminates artificial food colorings and preservatives that have become so common in the western diet. Some have claimed that the increasing use of colorings and preservatives explains why the prevalence of ADHD is greater in Western countries and has been increasing over time. But those people have it wrong. The prevalence of ADHD is similar around the world and has not been increasing over time. That has been well documented but details must wait for another blog.
The Feingold and other elimination diets have been studied by meta-analysis. This means that someone analyzed several well-controlled trials published by other people. Passing the test of meta-analysis is the strongest test of any treatment effect. When this test is applied to the best studies available, there is evidence that the exclusion of fool colorings helps reduce ADHD symptoms. But more restrictive diets are not effective. So removing artificial food colors seems like a good idea that will help reduce ADHD symptoms. But although such diets ‘work’, they do network very well. On a scale of one to 10where 10 is the best effect, drug therapy scores 9 to 10 but eliminating food colorings scores only 3 or 4. Some patients or parents of patients might want this diet change first in the hopes that it will work well for them. That is a possibility, but if that is your choice, you should not delay the more effective drug treatments for too long in the likely event that eliminating food colorings is not sufficient. You can learn more about elimination diets from Nigg, J. T., and K.Holton (2014). "Restriction and elimination diets in ADHD treatment."Child Adolesc Psychiatr Clin N Am 23(4): 937-953.
Keep in mind that the treatment guidelines from professional organizations point to ADHD drugs as the first-line treatment for ADHD. The only exception is for preschool children where medication is only the first-line treatment for severe ADHD; the guidelines recommend that other preschoolers with ADHD be treated with non-pharmacologic treatments, when available. You can learn more about non-pharmacologic treatments for ADHD from a book I recently edited: Faraone, S. V. &Antshel, K. M. (2014). ADHD: Non-Pharmacologic Interventions. Child AdolescPsychiatr Clin N Am 23, xiii-xiv.
Mindfulness has been defined as “intentionally directing attention to present moment experiences with an attitude of curiosity and acceptance.” Mindfulness-based interventions (MBIs) aim to improve mindfulness skills.
A newly-published meta-analysis of randomized controlled trials (RCTs) by a team of British neurologists and psychiatrists explores the effectiveness of MBIs in treating a variety of mental health conditions in children and adolescents. Among those conditions is the attention deficit component of ADHD.
A comprehensive literature search identified studies that met the following criteria:
1) The effects of mindfulness were compared against a control condition – either no contact, waitlist, active, or attention placebo. The waitlist means the control group receives the same treatment after the study concludes. Active control means that a known, effective treatment (as opposed to a placebo) is compared to an experimental treatment. Attention placebo means that controls receive a treatment that mimics the time and attention received by the treatment group but is believed not to have a specific effect on the subjects. Participants were randomly assigned to the control condition.
2) The MBI was delivered in more than one session by a trained mindfulness teacher, involved sustained meditation practice, and it was not mixed in with another activity such as yoga.
Eight studies evaluating attention deficit symptoms, with a combined total of 1,158 participants, met inclusion criteria. The standardized mean difference (SMD) was 0.19, with a 95% confidence range of 0.04 to 0.34 (p = .02). That indicates a small effect size for MBIs in reducing attention deficit symptoms. Heterogeneity was low (I2 = 35, p =.15), and the Egger test showed little sign of publication bias (p = 0.42).
When looking only at studies with active controls, five studies with a total of 787 participants yielded an SMD of 0.13, with a 95% confidence interval of -0.01 to 0.28 (p = .06), indicating a tiny effect size that failed to reach significance. Active controls most commonly received health education, with a few receiving social responsibility training or Hatha yoga.
Overall, this meta-analysis suggests limited effectiveness, especially when compared with active controls. If MBIs are effective for ADHD, their effect on symptoms is very small. Thus, such treatments should not be used in place of the many well-validated, evidenced-based therapies available. Whether longer periods of MBI (training times varied between 2 and 18 hours spread out over 2 to 24 weeks) might result in greater effect sizes remains unexplored
After noting that the association between ADHD and obesity has been called into question because of small sample sizes, wide age ranges, self-reported assessments, and inadequate attention to potential confounders, an Israeli study team set out "to assess the association between board-certified psychiatrist diagnoses of ADHD and measured adolescent BMI [body mass index] in a nationally represented sample of over one million adolescents who were medically evaluated before mandatory military service."
The team distinguished between severe and mild ADHD. It also focused on a single age group.
All Israelis are subject to compulsory military service. In preparation for that service, military physicians perform a thorough medical evaluation. Trained paramedics recorded every conscript's height and weight.
The study cohort was divided into five BMI percentile groups according to the U.S. Centers for Disease Control and Prevention's BMI percentiles for 17-year-olds, and further divided by sex: <5th percentile (underweight), 5th-49th percentile (low-normal), 50th-84th percentile (high normal), 85th-94th percentile (overweight) and ≥95th (obese). Low-normal was used as the reference group.
Adjustments were made for sex, birth year, age at examination, height, country of birth (Israeli or other), socioeconomic status, and education level.
In the fully adjusted results, those with severe ADHD were 32% more likely to be overweight and 84% more likely to be obese than their typically developing peers. Limiting results to Israeli-born conscripts made a no difference.
Male adolescents with mild ADHD were 24% more likely to be overweight, and 42% more likely to be obese. Females with mild ADHD are 33% more likely to be overweight, and 42% more likely to be obese. Again, the country of birth made no difference.
The authors concluded, that both severe and mild ADHD was associated with an increased risk for obesity in adolescents at the age of 17 years. The increasing recognition of the persistence of ADHD into adulthood suggests that this dual morbidity may have a significant impact on the long-term health of individuals with ADHD, thus early preventive measures should be taken.
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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