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October 21, 2025
Children and adolescents with ADHD tend to be less active and more sedentary than their typically developing peers. This is concerning, since physical activity benefits mental, physical, and social development. For youth with ADHD, being active can improve symptoms like inattention, working memory, and inhibitory control.
A major barrier to physical activity for children and adolescents with ADHD is limited motor competence. This stems from challenges in developing basic motor skills and more complex abilities needed for sports and advanced movements.
Difficulties in developing fundamental movement skills – such as locomotor (running, jumping), object-control (throwing, catching), and stability skills (balancing, turning) – can reduce motor competence and limit physical activity. These basic movements are learned and refined with practice and age, not innate abilities.
To date, research on the link between ADHD and motor competence has remained inconclusive. This systematic review and meta-analysis by a Spanish research team therefore aimed to determine whether children and adolescents with ADHD differ in motor competence from those with typical development (TD).
Studies had to include children and adolescents diagnosed with ADHD. They had to involve a full motor assessment battery, not just one test, and present motor competence data for both ADHD and TD groups.
The team excluded studies involving participants with other neurodevelopmental disorders or cognitive impairments, unless separate data for the ADHD subgroup were reported.
Meta-analysis of six studies combining 323 children and adolescents found that typically developing individuals were twelve times more likely to score in the 5th percentile of the Movement Assessment Battery for Children as their peers diagnosed with ADHD. They were also three times more likely to score in the 15th percentile (five studies, 289 participants). Results were consistent across the studies (low heterogeneity). All included studies were randomized.
Meta-analysis of five studies totaling 198 participants using the Test of Gross Motor Development reported significant deficits in both locomotor skills and object control skills among children and adolescents diagnosed with ADHD relative to their typically developing peers. In this case, however, results were inconsistent across studies (very high heterogeneity), and one of the studies was unrandomized. Because the team published only unstandardized mean differences, there was no indication of effect sizes.
Meta-analysis of two studies encompassing 164 participants using the Bruininks-Oseretsky Test of Motor Proficiency similarly yielded significant deficits among children and adolescents diagnosed with ADHD relative to their typically developing peers, but in this case with low heterogeneity. Notably, one of the two studies was not randomized.
Moreover, the team made no assessment of publication bias.
The team concluded, “The findings of this review indicate that children and adolescents with ADHD show significantly lower levels of motor competence compared to their TD peers. This trend was evident across a range of validated assessment tools, including the MABC, BOT, TGMD, and other standardized test batteries. Future research should aim to reduce methodological heterogeneity and further investigate the influence of factors such as ADHD subtypes and comorbid conditions on motor development trajectories.”
However, without a publication bias assessment, reliance on unrandomized studies in two of the tests, no indication of effect size in the same two tests, and small sample sizes, these results are at best suggestive, and will require further research to confirm.
Nerea Blanco-Martínez, Daniel González-Devesa, Carlos Ayán-Pérez, and José Carlos Diz-Gómez, “Differences in Motor Competence Between Children and Adolescents With and Without ADHD: Findings from a Systematic Review and Meta-analysis,” Journal of Autism and Developmental Disorders (2025), https://doi.org/10.1007/s10803-025-07033-1.
Computerized cognitive training (CCT) uses computers to try to strengthen cognitive skills and processes, reduce ADHD symptoms, and improve executive functioning. Executive functions are cognitive processes and mental skills that help individuals plan, monitor, and successfully execute their goals.
CCT programs target one or more cognitive processes such as motor inhibition, interference inhibition, sustained attention, and working memory. They ramp up task difficulty as performance improves. The goal is to harness the brain’s inherent adaptability (neuroplasticity) to boost performance.
A European study team that previously probed the efficacy of CCT through meta-analysis had been unable to provide a robust estimate of effect size due to an insufficient number of high-quality trials with probably blinded outcomes. Noting that “there have been a considerable number of new RCTs [randomized controlled trials] published, many with larger samples, well-controlled designs and blinded outcomes,” the team performed an updated systematic review and meta-analysis.
They included RCTs with participants of any age who either had a clinical diagnosis of ADHD or were above cut-off on validated ADHD rating scales. RCTs had to have been peer-reviewed and published in an academic journal, and to have reported a validated outcome measure of ADHD symptoms, neuropsychological processes, and/or academic outcomes.
Fourteen RCTs with a combined total of 631 participants had probably blinded outcomes. Meta-analysis of these studies yielded no significant effect on either overall ADHD symptoms or hyperactivity/impulsivity symptoms. There was a marginally significant reduction in inattention symptoms, but the effect size was small. Between-study variation (heterogeneity) was negligible and there was no sign of publication bias.
Regarding academic outcomes, meta-analyses revealed no gain in arithmetic ability or reading fluency. There was a small but not statistically significant improvement in reading comprehension. Heterogeneity was minimal, with no indication of publication bias.
With two related exceptions, meta-analyses of RCTs measuring executive functions likewise reported no significant improvements in attention, interference inhibition (initial stage in controlling impulsive behavior), motor inhibition (follow-up stage in controlling impulsive behavior), non-verbal reasoning, processing speed, and set shifting (the ability to unconsciously shift attention between one task and another).
The exceptions were for working memory tasks. Meta-analysis of 15 RCTs with a combined 753 participants reported a highly significant small-to-medium effect size improvement in verbal working memory. A separate meta-analysis of nine RCTs with a total of 441 participants similarly reported a highly significant improvement in visuospatial working memory, this time with medium effect size.
The team concluded, “There was no empirical support for the use of CCT as a stand-alone intervention for ADHD symptoms based on the largest and most comprehensive meta-analysis of RCTs conducted to date. Small effects, of likely limited clinical importance, on inattention symptoms were found – but these were limited to the setting in which the intervention was delivered. Robust evidence of small- to-moderate improvements in visual-spatial and verbal STM/WM tasks did not transfer to other domains of executive functions or academic outcomes.”
ADHD treatment usually involves a combination of medication and behavioral therapy. However, medication can cause side effects, adherence problems, and resistance from patients or caregivers.
Numerous systematic reviews and meta-analyses have evaluated the effects of non-pharmacological interventions on ADHD. With little research specifically examining game-based interventions for children and adolescents with ADHD or conducting meta-analyses to quantify their treatment effectiveness, a Korean study team performed a systematic search of the peer-reviewed medical literature to do just that.
The Study:
To be included, studies had to be randomized controlled trials (RCTs) that involved children and adolescents diagnosed with ADHD. The team excluded RCTs that included participants with psychiatric conditions other than ADHD.
Eight studies met these standards. Four had a high risk of bias.
Meta-analysis of four RCTs with a combined total of 481 participants reported no significant improvements in either working memory or inhibition from game-based digital interventions relative to controls.
Likewise, meta-analysis of three RCTs encompassing 160 children and adolescents found no significant improvement in shifting tasks relative to controls.
And meta-analysis of two RCTs combining 131 participants reported no significant gains in initiating, planning, organizing, and monitoring abilities, nor in emotional control.
The only positive results were from two RCTs with only 90 total participants that indicated some improvement in visuospatial short-term memory and visuospatial working memory.
There was no indication of effect size, because the team used mean differences instead of standardized mean differences.
Conclusion:
The team concluded, “The meta-analysis revealed that game-based interventions significantly improved cognitive functions: (a) visuospatial short-term memory … and (b) visuospatial working memory … However, effects on behavioral aspects such as inhibition and monitoring … were not statistically significant, suggesting limited behavioral improvement following the interventions.”
Simply put, the current evidence does not support the effectiveness of game-based interventions in improving behavioral symptoms of ADHD in children and adolescents. The only positive results were from two studies with a small combined sample size, which does not qualify as a genuine meta-analysis. All the other meta-analyses performed with larger sample sizes reported no benefits.
Youths with ADHD are known to be more prone to language problems when compared with typically developing peers. To what extent does that affect their ability to share a narrative with others?
A Danish research team conducted a systematic review and meta-analysis of the peer-reviewed medical literature to explore this question. They stressed that this ability is important because "a narrative is a genre of discourse - a form of social communication used to derive meaning from experiences and to construct a shared understanding of events. In other words, it is the fundamental ability of orally producing a coherent story." They focused on the production of narratives rather than comprehension.
Studies had to have a minimum of 10 participants. They had to compare aspects of oral narrative production in children and adolescents with either a formal ADHD diagnosis or a score above a clinical cut-off on a validated ADHD rating scale to a control group of typically developing youths. Youths with confirmed autism spectrum disorder (ASD) or language impairment diagnoses were excluded. There were no constraints on IQ.
The team found sixteen studies with a combined total of 1,015 youths that met these criteria and were suitable for meta-analysis.
They examined seven aspects of oral narrative production:
· Coherence: A story structure that is logical and easy to follow in cause and sequence. There is a clear beginning, middle, and end. There are goals, attempts, and outcomes. A meta-analysis of nine studies with a combined total of 750 participants found youths with ADHD less coherent than their typically developing peers, with a medium effect size. There was virtually no between-study heterogeneity and no sign of publication bias.
· Cohesion: This ensures referencing of events and characters in a manner that enables the listener to grasp how characters, events, and ideas in a story are related. Ambiguous or contradictory references get in the way of this. A meta-analysis of eight studies with a combined total of 501 participants found youths with ADHD showed less cohesion than their typically developing peers, with a medium effect size. Again, with virtually no between-study heterogeneity, and no sign of publication bias.
· Disruptions: These can be sequence errors, misinterpretations, embellishments, or confabulations - fabricating imaginary experiences as compensation for loss of memory. A meta-analysis of six studies with 389 participants found youths with ADHD had more disruptions than their typically developing peers, with a small-to-medium effect size. There was virtually no between-study heterogeneity and no sign of publication bias.
· Fluency: Best explained in terms of errors that interfere with this quality, such as false starts, repeating words or sentences, and abandoning sentences without completing them. A meta-analysis of four studies with 220 participants found no difference in fluency between youths with ADHD and their typically developing peers.
· Production: This is a measure of output -overall length of the story, number of sentences, number of words. After adjusting for evidence of publication bias, a meta-analysis of twelve studies with 645 participants found no difference here.
· Syntactic complexity: This includes the extent of vocabulary and the use of proper grammar. A meta-analysis of six studies with 272 participants found youths with ADHD displayed less syntactic complexity than their typically developing peers, with a small-to-medium effect size. There was virtually no between-study heterogeneity and no sign of publication bi
· Internal state language: References to perceptions, thoughts, beliefs, and feelings. There were only two studies with 130 participants, so no meta-analysis was performed.
The authors concluded, "the results from the current meta-analysis suggest that children with ADHD have impairments in their narrative language. In particular, children with ADHD produce narratives that are less coherent, less cohesive, less syntactically complex, and include more disruptive errors than typically developing children do."
ADHD is one of the most common neurodevelopmental disorders in children, yet anyone familiar with this disorder, from clinicians and researchers to parents and patients, knows how differently it can manifest from one individual to the next. One person diagnosed with ADHD may primarily struggle with focus and staying on-task; another may find it nearly impossible to regulate their impulses or even start tasks; a third may frequently find themselves frozen with overwhelm and subject to emotional reactivity…
These are not just variations in severity; they may reflect genuinely different patterns of brain organization.
Our current diagnostic system groups all of these presentations under a single label (ADHD), with three behavioral subtypes (Hyperactive, Inattentive, and Combined) defined by symptom checklists. This framework has real clinical value of course, but it was built from behavioral observation rather than neurobiology, and may leave room for substantial heterogeneity to remain unexplained. In a new study, published in JAMA Psychiatry, researchers asked whether it’s possible to identify distinct neurobiologically subgroups within ADHD by analyzing patterns of brain structure, and whether those subgroups would map onto meaningful clinical differences.
How the Brain Was Analyzed
Researchers analyzed structural MRI scans from 446 children with ADHD and 708 typically-developing children across multiple research sites. From each scan, they constructed a morphometric similarity network; that is, a map of how different brain regions resemble one another in their structural properties. These networks reflect underlying biological organization, including shared patterns of cellular architecture and gene expression across brain regions.
From each individual's network, the research team calculated three properties that capture how each brain region functions within the broader network: how many connections it has, how efficiently it communicates with other regions, and how well it bridges different functional communities in the brain. Regions that score highly on these measures are sometimes called "hubs" and they play particularly influential roles in how information is integrated across the brain.
Rather than comparing the ADHD group to controls as a whole and looking for average differences, they used a normative modeling approach. This works similarly to a growth chart in pediatric medicine: instead of asking whether a child is above or below the group average, it asks how much a given child deviates from the expected range for their age and sex. This allows for individual variation across the ADHD group rather than flattening it into a single average profile.
The team then applied a data-driven clustering algorithm to these individual deviation profiles, allowing the data to reveal whether subgroups of children with ADHD shared similar patterns of brain network atypicality, without using any clinical symptom information to guide the clustering.
The Results:
Three stable, reproducible subtypes emerged from this analysis.
The first subtype was characterized by the most widespread differences from the normative range, particularly in regions connecting the medial prefrontal cortex to the pallidum (a deep brain structure involved in motivation and emotional regulation). Children in this group had the highest levels of both inattention and hyperactivity/impulsivity, and over a four-year follow-up period showed more persistent difficulties with emotional self-regulation than the other groups. They also had a higher rate of mood disorder comorbidity during follow-up, though this difference did not reach statistical significance given the sample size. The brain deviation patterns of this subtype showed correspondence with the spatial distributions of several neurotransmitter systems, including serotonin, dopamine, and acetylcholine, all of which have been previously implicated in ADHD pathophysiology.
The second subtype showed alterations concentrated in the anterior cingulate cortex and pallidum, a circuit involved in action control and response selection. This subtype had a predominantly hyperactive/impulsive profile, and its brain deviation patterns were associated with glutamate and cannabinoid receptor distributions.
The third subtype showed more focal differences in the superior frontal gyrus, a region involved in sustained attention. This subtype had a predominantly inattentive profile, with brain patterns linked to a specific serotonin receptor subtype.
A particularly important observation was that these brain-derived groupings aligned with clinically meaningful symptom differences, even though no symptom information was used in the clustering process. The fact that an analysis of brain structure alone arrived at groupings that correspond to recognizable clinical patterns is meaningful evidence that these subtypes reflect genuine neurobiological differences rather than statistical noise.
Replication in an Independent Sample
Scientific findings are only as trustworthy as their ability to replicate. The research team tested this clustering model in an entirely independent cohort of 554 children with ADHD from the Healthy Brain Network, a large, publicly available dataset collected under different conditions. The three subtypes were successfully identified in this new sample, with strong correlations between the brain deviation patterns observed in the original and validation cohorts. Differences in hyperactivity/impulsivity across subtypes were consistent with the discovery cohort, providing meaningful external validation of the approach.
What This Does and Doesn't Mean
It is important to be clear about what these findings do and do not imply. This study does not establish that these three subtypes are categorically distinct biological entities with sharp boundaries. They probably represent distinguishable regions along an underlying continuum of neurobiological variation. The neurochemical associations reported are exploratory and spatial in nature; they describe correspondences between brain deviation maps and neurotransmitter receptor density maps derived from separate imaging studies, and do not directly establish that any particular neurotransmitter system is altered in each subtype, nor do they currently inform treatment decisions.
The samples were not entirely medication-naive, and the strict comorbidity exclusion criteria may limit how well these findings generalize to typical clinical populations where comorbidities are the rule rather than the exception. All data came from research sites in the United States and China, and broader generalizability remains to be established.
What the study does demonstrate is that structured neurobiological heterogeneity exists within the ADHD diagnosis, that it can be reliably detected using brain imaging and data-driven methods, and that it aligns with meaningful clinical differences. The subtype defined by the most extensive brain network differences and the most severe, persistent clinical profile may be of particular importance, representing a group that could benefit most from early identification and targeted support.
The longer-term goal of this line of research is to move toward a more biologically grounded understanding of ADHD that complements existing diagnostic approaches and that may ultimately help guide more individualized treatment decisions. That goal, for now, remains a research ambition rather than a clinical reality, but this study takes a meaningful step in that direction.
Managing high blood pressure requires more than just getting a prescription; it means taking medication consistently, day after day, often for years. For people with ADHD, that kind of routine can be genuinely difficult. In our new study, published in BMC Medicine, we set out to understand just how much ADHD affects whether people stick with their blood pressure medication, and whether ADHD treatment itself might make a difference.
Why This Question Matters
Hypertension affects nearly a third of adults worldwide and is one of the leading drivers of heart disease and stroke. At the same time, ADHD, long thought of as a childhood disorder, affects around 2.5% of adults and is increasingly recognized as a risk factor for cardiovascular problems, including high blood pressure. Yet no large-scale study had ever examined whether having ADHD affects how well people follow through with their blood pressure treatment. We wanted to fill that gap.
What We Did
We analyzed health records from over 12 million adults across seven countries, Australia, Denmark, the Netherlands, Norway, Sweden, the UK, and the US, who had started antihypertensive (blood pressure-lowering) medication between 2010 and 2020. About 320,000 of them had ADHD. We tracked two things: whether they stopped their blood pressure medication entirely within five years, and whether they were taking it consistently enough (covering at least 80% of days) over one, two, and five years of follow-up.
What We Found
Across nearly all countries, adults with ADHD were more likely to stop their blood pressure medication and less likely to take it consistently. Overall, those with ADHD had about a 14% higher rate of discontinuing treatment within five years, and were 45% more likely to have poor adherence in the first year, a gap that widened to 64% by the five-year mark. These patterns were most pronounced in middle-aged and older adults.
Interestingly, young adults with ADHD were actually slightly less likely to discontinue treatment than their peers without ADHD, a finding we think may reflect the fact that younger people with ADHD are often more actively engaged with healthcare systems, especially given the cardiovascular monitoring that comes with ADHD medication use.
Perhaps the most encouraging finding was this: among people with ADHD who were also taking ADHD medication, adherence to blood pressure treatment was substantially better. Those on ADHD medication were about 38% less likely to have poor adherence at one year, and nearly 50% less likely at five years. While we can't establish causation from this type of study, one plausible explanation is that treating ADHD, reducing inattention and impulsivity, makes it easier to maintain the routines that consistent medication use requires. It's also possible that people on ADHD medication simply have more regular contact with healthcare providers, which keeps other health problems better monitored and managed.
What This Means in Practice
The core ADHD symptoms of inattention and poor organization are precisely the traits that make long-term medication adherence difficult. Add in the complexity of managing multiple disorders and medications, and it's easy to see why people with ADHD face extra challenges. Our findings suggest that clinicians treating adults with ADHD for cardiovascular disorders should be aware of these challenges and consider tailored support strategies, things like regular follow-up appointments, patient education, and tools that help with routine and organization.
There's also a broader message here about the potential ripple effects of treating ADHD well. Supporting someone in managing their ADHD may not just improve their attention and daily functioning; it may also help them take better care of their physical health, including disorders as serious as hypertension.
Future research should explore which specific support strategies are most effective, and whether these findings hold in lower- and middle-income countries where the data don't yet exist.
If you or someone you know has ADHD, you may be familiar with the challenge of staying on medication. Stimulants like methylphenidate (Ritalin) are the most common and effective treatment for ADHD, but a surprisingly large number of people stop taking them within the first year. In our new study, published in Translational Psychiatry, we sought to determine whether a person's genetic makeup plays a role in the development of the disorder.
What We Did
We analyzed data from over 18,000 people with ADHD in Denmark, all of whom had started stimulant medication. We tracked whether they stopped treatment within the first year, defined as going more than six months without filling a prescription. Nearly 4 in 10 (39%) had discontinued by that point. We then looked at their genetic data to see whether DNA differences could help explain who was more likely to stop.
What We Found
The short answer is: genetics does play a role, but it's modest. No single gene had a dramatic effect. Instead, we found that a collection of small genetic influences—distributed across the genome—contributed to the likelihood of stopping treatment early.
One of the most consistent findings was that people with a higher genetic predisposition for psychiatric disorders like schizophrenia, depression, or general mental health difficulties were more likely to discontinue their medication. This was true across all age groups. Interestingly, having a higher genetic risk for ADHD itself was not associated with stopping treatment, suggesting that the genetics of having ADHD and the genetics of staying on medication are quite different things.
We also found that the genetic picture looks different depending on age. In children under 16, body weight genetics (BMI) played a surprising role, children with a genetic tendency toward higher weight were actually less likely to stop, possibly because stimulant-related appetite suppression is less of a problem for them. In older adolescents and adults, higher genetic potential for educational attainment and IQ was linked to staying on treatment, possibly reflecting better access to information and healthcare support.
On the rare variant side, we found a tentative signal that people who stopped treatment had fewer disruptive variants in genes involved in dopamine, the brain chemical that stimulants work on. This might mean that those who continue on medication genuinely have more disruption in their dopamine system and benefit more from stimulant treatment.
What This Means
Our findings suggest that stopping ADHD medication early isn't simply a matter of willpower or forgetting to take a pill. Biology matters. A person's broader genetic vulnerabilities, particularly for other psychiatric disorders, may make it harder to stay on treatment, perhaps because of side effects, poor response, or the complexity of managing multiple mental health challenges at once.
We're still far from being able to use genetics to predict who will stop their medication, the effects we found are real but small, and much of the variation in treatment persistence remains unexplained. But this work is a step toward understanding the biological foundations of treatment challenges in ADHD, and hopefully toward more personalized approaches to care in the future.
Larger studies and research that can distinguish why people stop (side effects versus poor response versus practical barriers), will be the next steps.
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