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September 17, 2025
Parents and teachers often ask: Does ADHD medication actually improve grades and school performance? The answer is: yes, but with important limitations. Medications are very effective at reducing inattention, hyperactivity, and impulsivity but their impact on long-term academic outcomes like grades and test scores is not as consistent.
In the Classroom
The medications for ADHD consistently: Improve attention, reduce classroom disruptions, increase time spent on-task and help children complete more schoolwork and homework. Medication can help children with ADHD access learning by improving the conditions for paying attention and persisting with work.
Does Medication Improve Test Scores and Grades?
This is where the picture gets more complicated. Medications have stronger effect on how much work is completed but a weaker effect on accuracy. Many studies show that children on medication attempt more problems in reading, math, and spelling, but the number of correct answers doesn’t always improve as much. Some studies find small but significant improvements in national exam scores and higher education entrance tests during periods when children with ADHD are medicated.
Grades improve, as well, but modestly. Large registry studies in Sweden show that students who consistently take medication earn higher grades than those who don’t. However, these gains usually do not close the achievement gap with peers who do not have ADHD.
Keep in mind that small improvements for a group as a whole mean that some children are benefiting greatly from medication and others not at all. We have no way of predicting which children will improve and which do not.
Medication Alone Isn’t Enough
Academic success depends on more than just reducing inattention, hyperactivity and impulsivity. Skills like organization, planning, studying, and managing long-term projects are also critical. Medication cannot teach these skills.
So, in addition to medication, the patient's treatment program should include educational support (tutoring, structured study skills programs), behavioral interventions (parent training, classroom management strategies), and accommodations at school (extra time, reduced distractions, organizational aids) Parents should discuss with their prescriber which of these methods would be appropriate.
Conclusions
ADHD medication is a powerful tool for reducing symptoms and supporting learning. It improves test scores and grades for some children, especially when taken consistently. But it is not a magic bullet for academic success. The best results come when medication is combined with educational and behavioral supports that help children build the skills they need to thrive in school and beyond.
Cortese, S., et al. (2018). Comparative efficacy and tolerability of medications for ADHD in children, adolescents, and adults: a systematic review and network meta-analysis. The Lancet Psychiatry, 5(9), 727–738.
Jangmo, A., et al. (2019). Attention-Deficit/Hyperactivity Disorder, School Performance, and Medication: A Swedish 9-Year Follow-Up Study. Journal of the American Academy of Child & Adolescent Psychiatry, 58(4), 423–432.
Kortekaas-Rijlaarsdam, A. F., et al. (2019). Does methylphenidate improve academic performance? A meta-analysis and study on the role of daily practice. European Child & Adolescent Psychiatry, 28(3), 357–370.
Lu, Y., et al. (2017). Association Between Medication Use and Performance on Higher Education Entrance Exams in ADHD. JAMA Psychiatry, 74(8), 815–822.
Molina, B. S. G., et al. (2009). The MTA at 8 Years: Prospective Follow-Up of Children Treated for Combined-Type ADHD in a Multisite Study. Journal of the American Academy of Child & Adolescent Psychiatry, 48(5), 484–500.
Pérez, T. V., et al. (2025). Long-term effect of pharmacological treatment on academic outcomes: a target trial emulation. International Journal of Epidemiology, 54(2).
Shaw, M., et al. (2012). A systematic review and analysis of long-term outcomes in ADHD: effects of treatment and non-treatment. BMC Medicine, 10, 99.
A German team of researchers performed a comprehensive search of the medical literature and identified 35randomized controlled trials (RCTs) published in English that explored this question. Participating children were between three and six years old. Children with intellectual disabilities, sensory disabilities, or specific neurological disorders such as epilepsy were excluded.
The total number of participating preschoolers was over three thousand, drawn almost exclusively from the general population, meaning these studies were not specifically evaluating effects on children with ADHD. But given that ADHD results in poorer executive functioning, evidence of the effectiveness of cognitive training would suggest it could help partially reverse such deficits.
RCTs assign participants randomly to a treatment group and a group not receiving treatment but often receiving a placebo. But RCTs themselves vary in risk of bias, depending on:
After evaluating the RCTs by these criteria, the team performed a series of meta-analyses.
Combining the 23 RCTs with over 2,000 children that measured working memory, they found that cognitive training led to robust moderate improvements. Looking only at the eleven most rigorously controlled studies strengthened the effect, with moderate-to-large gains.
Twenty-six RCTs with over 2,200 children assessed inhibitory control. When pooled, they indicated a small-to-moderate improvement from cognitive training. Including only the seven most rigorously controlled studies again strengthened the effect, boosting it into the moderate effect zone.
Twelve RCTs with over 1,500 participants tested the effects of cognitive training on flexibility. When combined, they pointed to moderate gains. Looking at only the four well-controlled studies boosted the effect to strong gains. Yet here there was evidence of publication bias, so no firm conclusion can be drawn.
Only four studies with a combined total of 119 preschoolers tested the effects on ADHD ratings. The meta-analysis found a small but non-significant improvement, very likely due to insufficient sampling. As the authors noted, "some findings of the meta-analysis are limited by the insufficient number of eligible studies. Specifically, more studies are needed which use blinded assessments of subjective ratings of ADHD ... symptoms ..."
The authors concluded that their meta-analyses revealed significant, mostly medium-sized effects of the preschool interventions on core EFs [executive functions] in studies showing the low risk of bias."
Monitoring the Future is a multicohort U.S. national longitudinal study of adolescents followed up into young adulthood.
The U.S. research team used data from this study to follow 5,034 twelfth graders over a period of six years, until they were 23 and 24 years of age.
Prescription stimulant misuse was assessed at baseline and each follow-up survey year by asking how often they used prescription stimulants without a physician’s orders. They were similarly asked about cocaine and methamphetamine use.
The study team adjusted for the following confounding variables: sex, race and ethnicity, parents’ level of education, urbanicity, U.S. region, cohort year, grade point average during high school, past-30-day cigarette use (at 18 years of age), past-2-week binge drinking (at 18), past-year marijuana use (at 18), past-year prescription opioid misuse (at 18), past-year prescription stimulant misuse (at 18), lifetime cocaine use (at 18), lifetime methamphetamine use (at 18), lifetime use of nonstimulant therapy for ADHD (at 18), and discontinued use of stimulant therapy for ADHD (at 18).
With these adjustments, they found that stimulant use for ADHD was in no way associated with subsequent cocaine use. In fact, it was associated with lesser odds of subsequent cocaine use, though the association was not statistically significant.
Likewise, they reported that stimulant use for ADHD was in no way associated with subsequent methamphetamine use.
On the other hand, those who used prescription stimulants without a physician’s orders were 2.6 times more likely to subsequently use either cocaine or methamphetamine.
The team concluded, “In this multicohort study of adolescents exposed to prescription stimulants, adolescents who used stimulant therapy for ADHD did not differ from population controls in initiation of illicit stimulant (cocaine or methamphetamine) use, which suggested a potential protective effect, given evidence of elevated illicit stimulant use among those with ADHD. In contrast, monitoring adolescents for PSM is warranted because this behavior offered a strong signal for transitioning to later cocaine or methamphetamine initiation and use during young adulthood.”
Noting that “little is known about whether school-level stimulant therapy for ADHD is associated with NUPS [nonmedical use of prescription stimulants] among US secondary school students,” a team of American researchers searched for answers in a nationally representative sample of 3,284 U.S. secondary schools with well over 150,000 high school students.
“Previous studies,” the authors continued, “have largely neglected school-level factors associated with NUPS among US secondary school students, including school size, school geographical location, school-level racial composition, school-level rates of substance use (eg, binge drinking), and school-level stimulant therapy for ADHD.”
In surveys, students were asked if they had ever taken stimulant medications for ADHD under a physician’s or health professional’s supervision, with three possible answers: no, yes but only in the past, and yes, currently. Responses for use in the past, and separately for current use, were combined and aggregated to the school level to reflect the percentage of the study body who used prescription stimulants for ADHD.
The surveys explored NUPS by asking, “On how many occasions (if any) have you taken amphetamines or other prescription stimulant drugs on your own—that is, without a doctor telling you to take them... in your lifetime?...during the last 12 months?...during the last 30 days?”
The study team controlled for sex, race and ethnicity, parental education, GPA, binge drinking, cigarette smoking, cannabis use, cohort year, school type, grade level, urbanicity, school size, US Census region, % of student body with low grades, % female, % with at least one parent with a college degree, % White, % binge drinking during past 2 weeks, % cigarette smoking in past 30 days, and % cannabis use during the past 30 days. The analysis also included individual-level medical use of stimulant therapy for ADHD history to estimate individual-level past-year NUPS. Finally, it included both individual-level and school-level risk factors to assess individual-level past-year NUPS.
With all these adjustments, at the individual level, both high school students presently on prescribed stimulant therapy for ADHD and those who had previously been on such prescribed therapy were more than twice as likely to engage in past-year NUPS as those who were never on prescribed stimulant medication.
Turning to the school level, in schools where 12% or more of students were on prescribed stimulant therapy for ADHD, students in general were 36% more likely to engage in past-year NUPS than in schools where none of the students were on prescribed stimulant therapy for ADHD.
This is not surprising, as it confirms that students who use prescription drugs for nonmedical often get their supply from fellow students who are prescribed those drugs.
While at the individual level, binge drinking, cigarette smoking, and cannabis use were strong predictors of NUPS, at the whole-school level they had no significant effect. A poor grade point average mildly increased risk in the individual, but high percentages of students with low grades had no effect on peer NUPS. Race and ethnicity made a difference at the individual level (NUPS significantly more likely among White students than Blacks and Hispanics), but made no difference at the school level.
The team concluded, “These findings suggest that school-level stimulant therapy for ADHD and other school-level risk factors were significantly associated with NUPS and should be accounted for in risk-reduction strategies and prevention efforts.”
ADHD is commonly treated with medication, but these treatments frequently cause side effects such as reduced appetite and disrupted sleep. Psychological and behavioral therapies exist as alternatives, but they tend to be expensive, hard to scale, and generally do little to address the motor difficulties that many children with ADHD experience — things like clumsy movement, poor handwriting, or difficulty with coordination.
Physical exercise has attracted attention as a more accessible option. But research findings have been mixed, partly because studies vary so widely in how exercise is delivered and what outcomes they measure. This meta-analysis, drawing on 21 studies involving 850 children and adolescents aged 5–20 with a clinical ADHD diagnosis, tries to cut through that noise.
Two types of motor skills
The researchers separated motor skills into two broad categories:
The Data:
Gross motor skills (16 studies, 613 participants)
Overall, exercise produced medium-to-large improvements in gross motor skills. The strongest gains were in:
No significant gains were found in balance or flexibility.
Fine motor skills (13 studies, 553 participants):
Exercise also produced medium-to-large improvements in fine motor skills, specifically:
The Results: What Kind of Exercise Works Best?
Two factors stood out consistently across both gross and fine motor skills: session length and frequency.
The type of exercise mattered; structured programs with clear motor-skill components (rather than unstructured physical activity) yielded stronger results.
These results are not without caveats, however. The authors urge caution in interpreting these findings. A few key limitations include:
The Bottom Line
This meta-analysis provides tentative moderate evidence that structured physical exercise can meaningfully support motor skill development in children and adolescents with ADHD — particularly when sessions run longer than 45 minutes and occur at least three times a week. The benefits appear most robust for object control, locomotion, handwriting, and manual dexterity.
That said, the evidence base still has real gaps. The authors call for better-designed, fully randomized controlled trials with consistent methods, standardized ways of measuring exercise intensity, and greater inclusion of children and adolescents who are not on medication — all of which would help clarify when, how, and for whom exercise works best.
Treatment guidelines for childhood ADHD recommend medications as the first-line treatment for most youth with ADHD. Still, concerns about side effects and long-term outcomes have increased interest in non-pharmacological approaches. Researchers at Saudi Arabian Armed Forces hospitals recently conducted a network meta-analysis comparing several interventions, including mindfulness-based therapy, cognitive behavioral therapy, behavioral parent training, neurofeedback, yoga, virtual reality programs, and digital working memory training.
Although the authors aimed to “provide a rigorous methodological approach to combine evidence from multiple treatment comparisons,” the study illustrates several pitfalls that arise when network meta-analysis is applied to a thin and heterogeneous evidence base.
What Network Meta-analysis Can and Cannot Do:
Network meta-analysis extends conventional meta-analysis by combining:
When the evidence network is large and well-connected, this approach can provide useful estimates of comparative effectiveness among many treatments.
This method is not always best, however, as many networks are sparse. This is especially true in areas such as complementary or behavioral therapies. In sparse networks, estimates rely heavily on indirect comparisons, and single studies can exert disproportionate influence over the results.
Conventional meta-analysis focuses on heterogeneity, meaning differences in results across studies within the same comparison.
Network meta-analysis must additionally evaluate consistency, whether the direct and indirect evidence agree.
However, when comparisons are supported by only one or two studies and the network is weakly connected, statistical tests for heterogeneity and consistency have very little power. In practice, this means the analysis often cannot detect problems even if they are present.
Sparse networks also make publication bias difficult to evaluate. This concern is particularly relevant in fields dominated by small trials and emerging therapies.
Why Such Treatment Rankings Are Appealing, but Potentially Problematic:
Many network meta-analyses summarize results using SUCRA, which estimates the probability that each treatment ranks best.
SUCRA, or Surface Under the Cumulative Ranking, is a key statistical metric in network meta-analyses. It is used to rank treatments by efficacy or safety. This is achieved by summarizing the probabilities of a treatment's rank into a single percentage, where a higher SUCRA value indicates a superior treatment. Ultimately, SUCRA helps pinpoint the most effective intervention among the ones compared.
Again, in well-supported networks, SUCRA can provide a useful summary of comparative effectiveness. But in sparse networks, rankings can create an illusion of precision, because treatments supported by a single small study may appear highly ranked simply due to random variation.
What Did this New Network Meta-analysis Study?
The study includes 16 trials with a total of 806 participants. But the structure of the evidence network is far weaker than this headline number suggests.
Based on the underlying studies:
This produces a very thin network, in which several interventions rely entirely on single studies.
Another challenge is that the included trials measure different outcomes. Some evaluate ADHD symptom severity, while others measure parental stress.
When studies use different outcome scales, meta-analysis typically relies on standardized measures such as the standardized mean difference to allow comparisons across studies. However, the analysis reports only mean-average differences, making it difficult to interpret the relative effect sizes.
Study Issues (including Limited Evidence and Risk of Bias):
The intervention supported by the largest number of studies (family mindfulness-based therapy) was one of the two approaches reported as producing statistically significant results. The other was BrainFit, which is supported by only a single previous trial.
Despite this limited evidence base, the study ranks interventions using SUCRA:
Notably, none of the runner-up interventions demonstrated statistically significant efficacy.
The authors acknowledge methodological limitations in the included studies:
“Blinding of participants and personnel (performance bias) exhibited notable concerns, as blinding for active treatment was not applicable in most studies.”
Such limitations are common in behavioral intervention trials, but they further increase uncertainty in already small evidence networks.
Conclusions:
The study ultimately concludes:
“This network meta-analysis supports MBT and BPT as effective non-pharmacological treatments for ADHD.”
However, the evidence underlying these claims is limited. Some analyses rely on very small numbers of studies and participants, and the network structure depends heavily on indirect comparisons.
Network meta-analysis can be a powerful tool when applied to a large, consistent, and well-connected body of evidence. When the evidence base is sparse, however, the resulting rankings and comparisons may appear statistically sophisticated while resting on a fragile evidentiary foundation.
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.
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