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Background:
Sleep is more than simple rest. When discussing sleep, we tend to focus on the quantity rather than the quality, how many hours of sleep we get versus the quality or depth of sleep. Duration is an important part of the picture, but understanding the stages of sleep and how certain mental health disorders affect those stages is a crucial part of the discussion.
Sleep is an active mental process where the brain goes through distinct phases of complex electrical rhythms. These phases can be broken down into non-rapid eye movement (NREM) and rapid eye movement (REM). The non-rapid eye movement phase consists of three stages of the four stages of sleep, referred to as N1, N2(light sleep), and N3(deep sleep). N4 is the REM phase, during which time vivid dreaming typically occurs.
Two of the most important measurable brain rhythms occur during non-rapid eye movement (NREM) sleep. These electrical rhythms are referred to as slow waves and sleep spindles. Slow waves reflect deep, restorative sleep, while spindles are brief bursts of brain activity that support memory and learning.
The Study:
A new research review has compiled data on how these sleep oscillations differ across psychiatric conditions. The findings suggest that subtle changes in nightly brain rhythms may hold important clues about a range of disorders, from ADHD to schizophrenia.
The Results:
People with ADHD showed increased slow-spindle activity, meaning those brief bursts of NREM activity were more frequent or stronger than in people without ADHD. Why this happens isn’t fully understood, but it may reflect differences in how the ADHD brain organizes information during sleep. Evidence for slow-wave abnormalities was mixed, suggesting that deep sleep disruption is not a consistent hallmark of ADHD.
Among individuals with autism spectrum disorder (ASD), results were less consistent. However, some studies pointed to lower “spindle chirp” (the subtle shift in spindle frequency over time) and reduced slow-wave amplitude. Lower amplitude suggests that the brain’s deep-sleep signals may be weaker or less synchronized. Researchers are still working to understand how these patterns relate to sensory processing, learning differences, or daytime behavior.
People with depression tended to show reduced slow-wave activity and fewer or weaker sleep spindles, but this pattern appeared most strongly in patients taking antidepressant medications. Since antidepressants can influence sleep architecture, researchers are careful not to overinterpret the changes. Nevertheless, these changes raise interesting questions about how both depression and its treatments shape the sleeping brain.
In post-traumatic stress disorder (PTSD), the trend moved in the opposite direction. Patients showed higher spindle frequency and activity, and these changes were linked to symptom severity which suggests that the brain may be “overactive” during sleep in ways that relate to hyperarousal or intrusive memories. This strengthens the idea that sleep physiology plays a role in how traumatic memories are processed.
The clearest and most reliable findings emerged in psychotic disorders, including schizophrenia. Across multiple studies, individuals showed: Lower spindle density (fewer spindles overall), reduced spindle amplitude and duration, correlations with symptom severity, and cognitive deficits.
Lower slow-wave activity also appeared, especially in the early phases of illness. These results echo earlier research suggesting that sleep spindles, which are generated by thalamocortical circuits, might offer a window into the neural disruptions that underlie psychosis.
The Take-Away:
The review concludes with a key message: While sleep disturbances are clearly present across psychiatric conditions, the field needs larger, better-standardized, and more longitudinal studies. With more consistent methods and longer follow-ups, researchers may be able to determine whether these oscillations can serve as reliable biomarkers or future treatment targets.
For now, the take-home message is that the effects of these mental health disorders on sleep are real and measurable.
Mayeli A, Sanguineti C, Ferrarelli F. Recent Evidence of Non-Rapid Eye Movement Sleep Oscillation Abnormalities in Psychiatric Disorders. Curr Psychiatry Rep. 2025 Dec;27(12):765-781. doi: 10.1007/s11920-024-01544-x. Epub 2024 Oct 14. PMID: 39400693.
Sleep disorders are one of the most commonly self-reported comorbidities of adults with ADHD, affecting 50 to 70 percent of them. A team of British researchers set out to see whether this association could be further confirmed with objective sleep measures, using cognitive function tests and electroencephalography (EEG).
Measured as theta/beta ratio, EEG slowing is a widely used indicator in ADHD research. While it occurs normally in non-ADHD adults at the conclusion of a day, during the day it signals excessive sleepiness, whether from obstructive sleep apnea or from neurodegenerative and neurodevelopmental disorders. Coffee reverses EEG slowing, as do ADHD stimulant medications.
Study participants were either on stable treatment with ADHD medication (stimulant or non-stimulant medication), or on no medication. Participants had to refrain from taking any stimulant medications for at least 48 hours prior to taking the tests. Persons with IQ below 80 or with recurrent depression or undergoing a depressive episode were excluded.
The team administered a cognitive function test, The Sustained Attention to Response Task (SART). Observers rated on-task sleepiness using videos from the cognitive testing sessions. They wired participants for EEG monitoring.
Observer-rated sleepiness was found to be moderately higher in the ADHD group than in controls. Although sleep quality was slightly lower in the sleepy group than in the ADHD group, and symptom severity slightly greater in the ADHD group than the sleepy group, neither difference was statistically significant, indicating extensive overlap.
Omission errors in the SART were strongly correlated with sleepiness level, and the strength of this correlation was independent of ADHD symptom severity. EEG slowing in all regions of the brain was more than 50 percent higher in the ADHD group than in the control group and was highest in the frontal cortex.
Treating the sleepy group as a third group, EEG slowing was highest for the ADHD group, followed closely by the sleepy group, and more distantly by the neurotypical group. The gaps between the ADHD and sleepy groups on the one hand, and the neurotypical group on the other, were both large and statistically significant, whereas the gap between the ADHD and sleepy groups was not. EEG slowing was both a significant predictor of ADHD and of ADHD symptom severity.
The authors concluded, These findings indicate that the cognitive performance deficits routinely attributed to ADHD are largely due to on-task sleepiness and not exclusively due to ADHD symptom severity. We would like to propose a simple working hypothesis that daytime sleepiness plays a major role in cognitive functioning of adults with ADHD. As adults with ADHD are more severely sleep deprived compared to neurotypical control subjects and are more vulnerable to sleep deprivation, in various neurocognitive tasks they should manifest larger sleepiness-related reductions in cognitive performance. One clear testable prediction of the working hypothesis would be that carefully controlling for sleepiness, time of day and/or individual circadian rhythms, would result in substantial reduction in the neurocognitive deficits in replications of classic ADHD studies.
A team of Spanish researchers performed a systematic search of the medical literature and found 28 studies that could be included in a series of meta-analyses of specific measures of sleep impairment. Except for a single meta-analysis with eight studies and 1,713 participants, however, all involved just three to five studies apiece, with anywhere from 121 to just over a thousand participants.
The team examined three sorts of measures:
· Subjective measures, based on self-reporting by ADHD patients.
· Polysomnography is an objective sleep study in which the subject is wired up and studied by technicians in a lab, usually overnight, monitoring multiple body functions, such as brain activity, eye movements, muscle activation, and heart rhythm.
· Actigraphy, a non-invasive objective means of monitoring sleep. The subject wears an actimetry monitor, which is usually worn like a wristwatch on the non-dominant arm. Because it is minimally intrusive, the subject may wear it for a week or more while engaging in normal activities.
In the subjective measures, adults with ADHD generally reported substantially higher sleep impairments than non-ADHD controls. In the largest meta-analysis, covering eight studies and 1,713 participants, adults with ADHD reported moderately longer latency times for falling asleep than controls. In meta-analyses of five studies with between 834 and 1,130 participants, they also reported moderately poorer sleep quality, more frequent night awakenings, being moderately less rested upon awakening in the morning, and moderate-to-strongly greater daytime sleepiness. There was no significant difference in perceived sleep duration.
Polysomnography measures, on the other hand, failed to confirm these subjective impressions. No significant differences were found between adults with ADHD and controls for the initial latency period until onset of sleep, sleep efficiency, waking after the onset of sleep, total sleep time, stage one or stage two sleep, slow-wave sleep, REM (rapid eye movement) sleep, and latency period until REM sleep.
As mentioned above, polysomnography is conducted in lab settings, and therefore inevitably diverges from normal patterns of behavior. Actigraphy helps bridge that gap, by monitoring normal behavior, though with more limited types and precision of data analysis.
And indeed, a meta-analysis of four studies with 222 participants confirmed self-reports that sleep efficiency was moderate to strongly lower in adults with ADHD and that the latency period until the onset of sleep was markedly longer. On the other hand, it found no significant difference in true sleep.
The researchers also looked at prevalence statistics. Whereas the prevalence of sleep-onset insomnia in the general population has been reported in the range of 13 to 15 percent, a meta-analysis of four studies with 466 participants found fully two-thirds of adults with ADHD reporting insomnia, a greater than four-to-one ratio. Similarly, a meta-analysis of three studies with 458 participants found one-third reporting daytime sleepiness, which is twice the rate reported in the general population.
There was no sign of publication bias in any of these results. The authors cautioned, however, about the small number of studies involved, stating this "compromises the generalizability of the findings." Also, some studies included patients undergoing pharmacological treatment for ADHD, "increasing the risk of confounding results."
Moreover, "Sleep onset latency and sleep efficiency were not significantly impaired in the polysomnography, which was incongruent with the actigraphy results. This may be due to a difference in the evaluation context. Whereas polysomnography is considered the gold-standard measure to objectively assess sleep architecture, actigraphy shows a more ecological approach, with the evaluation being conducted in a more naturalistic context for a longer period. However, actigraphy has more environmental influence, which can compromise the data recorded and the interpretation of the results, whereas, in polysomnography, multiple variables can be controlled in the laboratory setting to increase the internal validity of the results. On the contrary, polysomnography studies can produce artifacts due to the unusual circumstances in the setting, so results may need to be interpreted with caution."
The authors concluded, "The results found in the present study show the relevance of addressing sleep concerns in adult populations diagnosed with neurodevelopmental conditions."
We are only beginning to explore how ADHD affects sleep in adults. A team of European researchers recently published the first meta-analysis on the subject, drawing on thirteen studies with 1,439 participants. They examined both subjective evaluations from sleep questionnaires and objective measurements from actigraphy and polysomnography. However, due to differences among the studies, only two to seven could be combined for any single topic, generally with considerably fewer participants (88 to 873).
Several patterns emerged. Looking at results from sleep questionnaires, they found that adults with ADHD were far more likely to report general sleep problems (very large SMD effect size 1.55). Getting more specific, they were also more likely to report frequent night awakenings(medium effect size 0.56), taking longer to get to sleep (medium-to-large effect size 0.67), lower sleep quality (medium-to-large effect size 0.69), lower sleep efficiency (medium effect size 0.55), and feeling sleepy during the daytime(large effect size 0.75).
There was little to no sign of publication bias, though considerable heterogeneity on all but night awakenings and sleep quality.
Actigraphy readings confirmed some subjective reports. On average, adults with ADHD took longer to get to sleep (large effect size 0.80) and had lower sleep efficiency (medium-to-large effect size 0.68). They also spent more time awake (small-to-medium effect size 0.40). There was little to no sign of publication bias and there was little heterogeneity among studies.
None of the polysomnography measurements, however, found any significant differences between adults with and without ADHD. All effect sizes were small (under 0.20), and none came close to being statistically significant.
There were four instances where measurement criteria overlapped those from actigraphy and self-reporting, with varying degrees of agreement and divergence. There was no significant difference in total sleep time, matching findings from both the questionnaires and actigraphy. On percent time spent awake, polysomnography found little to no effect size with no statistical significance, whereas actigraphy found a small-to-medium effect size that did not quite reach significance, and self-reporting came up with a medium effect size that was statistically significant. Sleep onset latency and sleep efficiency, for which questionnaires and actigraphy found medium-to-large effects, the polysomnography measurements found little to none, with no statistical significance.
Polysomnography found no significant differences in stage 1-sleep, stage 2-sleep, slow-wave sleep, and REM sleep. Except for slow-wave sleep, there was no sign of publication bias. Heterogeneity was generally minimal.
One problem with the extant literature is that many studies did not take medication status into account.
The authors concluded, "future studies should be conducted in medicatio- naïve samples of adults with and without ADHD matched for comorbid psychiatric disorders and other relevant demographic variables."
In summary, these findings provide robust evidence that ADHD adults report a variety of sleep problems. In contrast, objective demonstrations of sleep abnormalities have not been consistently demonstrated. More work in medication-naïve samples is needed to confirm these conclusions.
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Language is powerful. The words we choose not only reflect our understanding of the world but also actively shape it. Recently, this truth has been at the center of a growing debate in the mental health field regarding how we talk about ADHD.
In a recent paper published in The Lancet Psychiatry titled “The Power of Words: Respectful Language in ADHD Research,” French and colleagues advocated for a shift toward "neurodiversity-affirmative language”. Rooted in the social model of disability, their proposal encourages researchers to abandon traditional medical terminology, e.g., words like disorder and deficit, in favor of more neutral terms such as condition and challenge.
My colleague, Dr. Michael Miller, and I read this with great interest. We completely agree that revising language is essential to good science and that, both as researchers and as human beings, we are ethically bound to speak respectfully. However, we felt compelled to write a response. In our new paper, we argue that while language must evolve, it must do so scientifically.
The Two Prerequisites for Language Change
If we are going to fundamentally shift our scientific lexicon, two requirements must be met:
Currently, the proposal by French and colleagues meets neither requirement. While they claim consensus is accumulating that certain terms are disrespectful, they provide zero empirical evidence that this view is shared by the community of individuals living with ADHD. Even proponents of patient-centered language admit there is surprisingly little data supporting specific language changes.
More alarmingly, the recommended changes severely dilute the scientific accuracy of our field. Let’s look at two examples.
Why a "Deficit" is Not Just a “Challenge"
French and colleagues suggest replacing the term deficit with challenge. On the surface, challenge sounds softer and more affirming. But scientifically, these words are not interchangeable.
For decades, the term deficit has been defined by a specific performance metric that falls substantially below an expected level. It is a measurable reality. A challenge, on the other hand, refers to a new or difficult task that tests someone's ability.
Every single human being is "challenged" by complex neuropsychological tests, but only some individuals who face that challenge demonstrate scientifically significant deficits. If we relabel measurable deficits as universal challenges, we sacrifice the exactness required to communicate scientific findings and accurately measure the effects of life-changing treatments.
ADHD is a Disorder, Not Just a "Condition"
Another proposal is to replace the word disorder with condition.
In mainstream psychiatry, a disorder is a clinically significant disturbance that causes distress or disability. The word purposefully separates natural human variation from the suffering (pathos) that gives pathology its meaning.
Condition is a completely neutral term. Pregnancy is a condition. Being tall is a condition. Calling ADHD a condition distances the diagnosis from the profound suffering it can cause.
French et al. argue against framing ADHD as a disorder because it exists on a spectrum without a clear cutoff, its manifestation is context-dependent, and its definition evolves. But if we apply that logic across all of medicine, the concept of disease unravels:
The Real-World Danger of Imprecise Language
This is not merely an academic debate over semantics. The language we use has real-world implications. In the United States and across the globe, our healthcare, educational, and legal systems run on precise medical language. Terms like impairment, dysfunction, and disorder are legally and administratively required to justify support services, workplace accommodations, specialized educational therapies, and medications. The language of pathology in diagnostic manuals regulates the flow of these resources.
If we reclassify ADHD as a neutral condition characterized only by challenges, we risk erecting massive bureaucratic barriers. Imprecise language could easily be used by institutions or insurance companies to deny vital care to the people who need it most.
The Need for Lexical Discipline
Attempting to characterize a clinical disorder entirely through its strengths happens in a scientific vacuum. We cannot ignore the vast body of rigorous evidence confirming that ADHD meets the long-standing criteria used by mental health science to identify clinical disorders.
As professionals, our respect for the ADHD community demands a commitment to language that is clear, correct, and evidence-based. To build genuine consensus about how we talk about ADHD, we need meaningful, collaborative dialogue that integrates compelling empirical data and rigorous theory.
This standard of "lexical discipline" is not just a technical preference. It is a vital mechanism through which science and the mental health professions uphold their duty to society.
For many ADHD patients, getting properly diagnosed and starting meds is only half the battle. The next step is figuring out the exact right dose. Historically, clinical guidelines have provided scant guidance on this critical step. This lack of direction can inadvertently foster two extremes in clinical practice: therapeutic inertia (settling for a subtherapeutic dose that leaves symptoms undertreated) or uncritical escalation (driving doses higher and higher beyond licensed limits without meaningful benefit).
To clear up this pharmacological gray area, an international team of researchers published the first comprehensive dose-effect network meta-analysis of ADHD medications in The Lancet Psychiatry. By pulling together a massive vault of clinical trial data, they mapped out exactly how efficacy and tolerability shift as doses increase.
Traditional meta-analyses evaluate head-to-head, pairwise data, comparing one drug at a specific dose directly against a placebo. However, this study utilized an advanced Bayesian hierarchical network model using restricted cubic splines.
This mathematical framework allowed the researchers to combine both direct trial data and indirect evidence simultaneously across 113 double-blind randomized controlled trials (RCTs). In total, the study evaluated data from 14,138 children/adolescents and 11,016 adults. By standardizing various formulations into basic equivalents (e.g., converting amphetamines to dextroamphetamine equivalents), they created a clear, unified map of dose ranges.
The study yielded distinct dose-response curves depending on the patient's age and the specific medication class. Rather than a linear trend in which "more medicine equals more benefit," most treatments reach a clear statistical plateau or ceiling.
For Children and Adolescents (under 18)
In the pediatric population, medications hit clear peak efficacy boundaries:
For both amphetamines and guanfacine, escalating the dosage past these points resulted in U-shaped curves, meaning further dose hikes yielded diminishing group-level symptom reduction.
For Adults (18 and older)
Adult profiles showed slightly different trajectories:
The ultimate goal of this landmark analysis is to guide shared decision-making between clinicians, patients, and families. The results send a dual message to the medical community:
A medication's true efficacy hinges on its tolerability, typically measured by how often patients discontinue treatment due to severe side effects. For amphetamines, this dropout risk scales linearly with dosage, notably exceeding placebo in children above 25 mg/day and becoming prominent in adults past 50 mg/day. In contrast, methylphenidate shows no clear dose-dependent dropout risk in pediatric patients, whereas adults face a steep risk curve: increasing the dose from 60 mg/day to 90 mg/day raises the dropout risk from 7.3% to 10.0% for only modest symptom relief. Finally, youth taking guanfacine experience a sharp climb in discontinuation risks, reaching a 9.8% median risk at 4 mg/day before data limitations obscure further trends.
The authors strongly emphasize that these findings represent group averages. Because individual metabolism, genetics, and comorbidities vary widely, some specific patients may legitimately require and tolerate higher off-label doses. However, if an unusually high dose is needed, the study suggests it should prompt a careful clinical pause, either to reassess for co-occurring conditions (like anxiety, autism, or sleep disorders) or to manage realistic expectations regarding what the medication can achieve.
The persistent shortage of ADHD medications has been more than a simple annoyance for patients at the pharmacy; the inconsistent availability of these medications has had deep impacts on the daily lives of those struggling without them. While public discourse has pointed fingers at over-prescribing or at restrictive DEA quotas, a recent economic evaluation in JAMA Health Forum suggests we’ve been looking in the wrong direction for an answer to what is causing this.
The reality of the shortage is less about increased demand and more about a fragile, globalized supply chain that snapped at a critical link.
Debunking the "Quota Myth":
The prevailing narrative suggested that the Drug Enforcement Administration (DEA) was stifling production by refusing to raise quotas. However, the data tells a different story. In 2022, manufacturers collectively met only about 70% of their allotted production quotas.
So we know that the problem wasn't that this DEA quota ceiling was too low. In fact, most manufacturers couldn't even reach it. Even when accounting for exports and domestic retail, production remained significantly below the legal limit. Even if the DEA had doubled its quotas, these medications still likely wouldn't have magically appeared on pharmacy shelves.
The most striking finding in the study is the correlation between the shortage and a sharp decline in the import of raw Active Pharmaceutical Ingredients (APIs). For the past decade, Germany has accounted for over 85% of US amphetamine imports. In 2022, these imports dropped by approximately 36.7%. When the API doesn't arrive at the factory, production for medium and small manufacturers grinds to a halt. Unlike larger pharmaceutical giants, these smaller players often lack the inventory cushion or flexibility to quickly pivot to a new supplier.
When the primary supply of amphetamine-based stimulants (like Adderall) faltered, it triggered a secondary crisis. Patients and clinicians, seeking alternatives, shifted toward lisdexamfetamine (Vyvanse) and methylphenidate (Ritalin/Concerta).
If we view this shortage purely through a regulatory or clinical lens, we miss the underlying cause of the crisis. The pharmaceutical industry has become a victim of its reliance on "just-in-time manufacturing” and highly concentrated sourcing. Because over 30% of APIs for the US market are produced in just one or two facilities globally, the system isn't just inefficient; it’s brittle. We are, in a sense, trapped in a system that prioritizes cost-reduction over the resilience required for public health.
The researchers suggest several policy shifts to prevent a repeat of this supply chain failure:
The ADHD medication shortage wasn't a failure of clinical oversight or a sudden surge in "TikTok-driven diagnoses”, as many have suggested. It was a failure of logistics. It reminds us that the path from a lab in Germany to a patient's hand in the US is far more precarious than we realized.
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