The State of the Art on Identifying and Treating Persons with Comorbid ADHD and Substance Use Disorders

Stimulant medications, such as methylphenidate (Ritalin) and amphetamines (Adderall),  are among the most widely prescribed drugs in the world. In the United States alone, prescription rates have climbed more than 50% over the past decade, driven largely by growing awareness of ADHD in both children and adults. Yet stimulants also have a long history of non-medical use, and concerns about their psychological risks persist among patients, families, and clinicians alike. 

Two major studies now offer the clearest picture yet of what that risk actually looks like, and who it may affect.

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The Background: 

Before turning to the research, it helps to understand the landscape. A notable share of stimulant users misuse their medication: roughly one in four takes it in ways other than prescribed, and about one in eleven meets criteria for Prescription Stimulant Use Disorder (PSUD). Counterintuitively, most people with PSUD aren’t obtaining drugs illicitly — they’re misusing their own prescriptions. 

This distinction between therapeutic and non-therapeutic use turns out to be critical when evaluating psychosis risk. 

The Study: 

A comprehensive meta-analysis by Jangra and colleagues pooled data across more than a dozen studies to compare psychotic outcomes in people using stimulants therapeutically versus non-therapeutically. The contrast was striking. 

Among therapeutic users  (more than 220,000 individuals taking stimulants at prescribed doses under medical supervision), psychotic episodes occurred in roughly one in five hundred people. When symptoms did appear, they typically emerged after prolonged treatment or in individuals with pre-existing psychiatric vulnerabilities, and they usually resolved when the medication was stopped. 

Among non-therapeutic users  (over 8,000 participants across twelve studies, many using methamphetamine or high-dose amphetamines), nearly one in three experienced psychotic symptoms. These episodes tended to be more severe, involving persecutory delusions and hallucinations, with faster onset and a greater likelihood of recurrence or persistence. 

The biology underlying this difference is well understood. When stimulants are taken orally at guideline-recommended doses, they produce moderate, gradual changes in neurotransmitter activity central to attention and executive functions. The brain tolerates these changes relatively well. Non-therapeutic use, by contrast, often involves much higher doses that are frequently delivered through non-oral routes such as injection or smoking. This produces a rapid, excessive surge in dopamine activity, which is precisely the neurochemical pattern associated with psychotic symptoms. 

The takeaway here is not that therapeutic stimulant use is risk-free, but that risk is strongly modulated by dose, route of administration, and individual psychiatric history. Clinicians are advised to monitor patients with pre-existing mood or psychotic disorders, particularly carefully. 

A Nationwide Study Focuses on Methylphenidate Specifically:

Where the meta-analysis cast a wide net, a large-scale population study by Healy and colleagues drilled into a specific and clinically pressing question: does methylphenidate (the most commonly prescribed ADHD medication, also known as Ritalin) increase the risk of developing a psychotic disorder? 

To find out, the researchers analyzed Finland's national health insurance database, tracking nearly 700,000 individuals diagnosed with ADHD. Finland's single-payer system made this kind of comprehensive, long-term tracking possible in a way that fragmented healthcare systems rarely allow. 

Critically, the team adjusted for a range of confounding factors that have clouded previous research, including sex, parental education, parental history of psychosis, and the number of psychiatric visits and diagnoses prior to the ADHD diagnosis itself (a proxy for illness severity). After these adjustments, they found no significant difference in the risk of schizophrenia or non-affective psychosis between patients treated with methylphenidate and those who remained unmedicated. This held true even among patients with four or more years of continuous methylphenidate use. 

The Take-Away: 

When considered together, these studies offer meaningful reassurance without encouraging complacency. 

For patients and families weighing ADHD treatment, the evidence suggests that methylphenidate used as prescribed does not increase psychosis risk, even over years of use. The rare cases of stimulant-associated psychosis in therapeutic settings are typically linked to high doses, pre-existing vulnerabilities, or both, and tend to resolve with discontinuation. 

For clinicians, the findings reinforce the importance of baseline psychiatric assessment before initiating stimulant therapy, ongoing monitoring in patients with mood or psychotic disorder histories, and clear patient education about the risks of dose escalation or non-oral use. 

The picture that emerges is one of a meaningful distinction between a medication used carefully within its therapeutic window and a drug misused outside of it. This distinction matters enormously when communicating risk to patients, policymakers, and the public. 

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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: 

• Gross motor skills — movements involving large muscle groups, such as running, jumping, throwing, and maintaining balance 

• Fine motor skills — precise, controlled movements, typically of the hands and fingers, such as handwriting and manual dexterity (the ability to handle objects skillfully) 

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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: 

• Object control (e.g., throwing, kicking) — large improvement 

• Locomotion (e.g., running, swimming), body coordination, and strength — medium improvements 

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: 

• Handwriting: large improvement 

• Manual dexterity: medium-to-large improvement 

• Hand-eye coordination: moderate improvement 

The Results: What Kind of Exercise Works Best? 

Two factors stood out consistently across both gross and fine motor skills: session length and frequency. 

• Sessions longer than 45 minutes produced roughly twice the benefit of shorter sessions 

• Three or more sessions per week outperformed less frequent programs for gross motor gains 

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: 

• Potential Publication Bias:  Studies showing positive results are more likely to be published, which can inflate apparent benefits. For gross motor skills, adjusting for this bias reduced the effect size from medium-to-large,  to medium. 

• Active vs. Passive Controls: When exercise was compared against doing nothing (a passive control), improvements looked significant. When compared against regular school activities (an active control), the gains were no longer statistically significant. This is a meaningful distinction: it suggests exercise may be beneficial, but not dramatically more so than simply being physically active in a structured school setting. 

• Medication status: Most participants were taking ADHD medication, so it’s unclear how well these findings apply to unmedicated children who might stand the most to benefit from structured exercise. 

• Study quality: Many studies lacked proper randomization, weakening confidence in the conclusions. 

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. 

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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: 

• Direct comparisons (treatment A vs. treatment B tested in clinical trials), and 

• Indirect comparisons (A vs. B inferred through a common comparator such as placebo or usual care). 

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: 

• Six interventions are supported by a single trial each (digital cognitive mindfulness training, BrainFit, neurofeedback, online mindfulness-based program, cognitive behavioral therapy, and working-memory training) 

• Three interventions are supported by two trials each 

• Only one intervention is supported by three trials (family mindfulness-based therapy) 

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: 

• Family MBT: 92% probability of being best 

• Behavioral parent training (BPT): 65% 

• Online mindfulness program: 49% 

• Cognitive behavioral therapy: 48% 

• Yoga: 39% 

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.

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