The study drew on 461 participants from the Wisconsin Sleep Cohort, a community-based longitudinal project that has tracked sleep health in Wisconsin adults since the late 1980s. For this analysis, the researchers used data collected between 2019 and 2023, when participants first completed the Sleep Inertia Questionnaire, a validated 23-item instrument that probes the cognitive, behavioral, emotional, and physiological dimensions of the sleep-to-wake transition. Participants averaged 73.8 years of age, were slightly more likely to be male, and were predominantly non-Hispanic white with at least some college education. Crucially, the cohort’s decades of infrastructure meant the researchers could adjust their statistical models for an unusually rich set of confounders, including body mass index, depression scores, alcohol and caffeine use, smoking history, chronotype, habitual sleep duration, and objectively measured sleep apnea severity from overnight polysomnography.

Cognitive performance was assessed with a standardized battery of six widely used neuropsychological tests, yielding seven outcome measures. The battery included the Rey Auditory Verbal Learning Test for memory, the Grooved Pegboard for fine motor speed and dexterity, Trail Making Test Part B for executive function and cognitive flexibility, the Symbol Digit Modalities Test for processing speed, the Controlled Oral Word Association Test for verbal fluency, and the Digit Cancellation Test for attention. Testing sessions lasted roughly 35 minutes and were deliberately scheduled during typical waking hours—usually late morning, mid-afternoon, or evening—rather than immediately upon awakening, with testing beginning an average of nearly 11 hours after participants reported waking on the day of assessment.

The results were striking in their selectivity. Higher scores on the Sleep Inertia Questionnaire were significantly associated with slower completion times on the Grooved Pegboard and Trail Making Test Part B, and with fewer correct responses on the Symbol Digit Modalities Test in unadjusted analyses. After the researchers statistically controlled for the full panel of demographic, psychosocial, and sleep-related covariates, the associations with the Grooved Pegboard and Trail Making Test Part B remained robust. These two tasks share a common thread: both depend heavily on psychomotor speed and executive functioning, suggesting that sleep inertia may be most closely tied to the brain’s capacity for rapid, coordinated, goal-directed processing rather than to memory or language abilities.

Perhaps the most intriguing finding was what did not predict cognition. The Epworth Sleepiness Scale, the gold-standard self-report measure of daytime sleepiness, showed no significant associations with any cognitive outcome. The Hypersomnia Severity Index, a broader measure of hypersomnia symptoms and impairment, produced only a single weak association—and it ran in a seemingly paradoxical direction, with greater hypersomnia severity linked to slightly better immediate verbal recall. The contrast implies that sleep inertia is not simply another face of general sleepiness. Instead, it appears to be a specific symptom with its own relationship to brain function, one that broader somnolence questionnaires may dilute or miss entirely.

Secondary analyses of the questionnaire’s four subscales sharpened the picture. The physiological, cognitive, and emotional subscales all showed significant associations with cognitive performance, particularly on the Grooved Pegboard and Trail Making Test Part B, with the physiological and cognitive subscales also linked to slower performance on the Digit Cancellation Test and reduced output on the Symbol Digit Modalities Test. The responses subscale, which captures behaviors such as waking to an alarm, showed no significant relationships—a pattern the authors attribute in part to the fact that most participants were retired and less likely to encounter the situations those items describe. Sensitivity analyses confirmed that the findings held when accounting for APOE4 carrier status, a major genetic risk factor for Alzheimer’s disease, and when excluding the small number of night-shift workers in the sample.

Why would the severity of morning grogginess relate to cognitive performance hours later? The biology of sleep inertia remains incompletely understood, but leading hypotheses center on the neural machinery of the sleep-to-wake transition itself. Awakening is not a single switch but a staggered cascade: brainstem arousal systems activate rapidly, while cortical regions—especially prefrontal areas supporting executive function—come online more slowly, and the functional segregation between task-positive and task-negative brain networks is temporarily lost. Proposed contributors include the slow clearance of sleep-promoting substances such as adenosine after waking, disrupted cortical arousal, circadian misalignment, and awakening from slow-wave sleep or during the biological night, all of which can intensify inertia. Pathologically severe sleep inertia, as seen in idiopathic hypersomnia, may reflect dysregulation of these transition mechanisms.

The connection to neurodegeneration is speculative but biologically plausible, and it is where the study becomes genuinely provocative. The locus coeruleus, the brain’s primary noradrenergic nucleus, is a key regulator of transitions between sleep and wakefulness and between REM and non-REM sleep—and it is among the earliest sites of pathological tau accumulation in Alzheimer’s disease, becoming hypoactive during preclinical stages. Impaired monoaminergic neurotransmission upon awakening could, in theory, mediate prolonged episodes of sleep inertia. Adding an intriguing circumstantial link, caffeine—an adenosine receptor antagonist—reduces sleep inertia in sleep-deprivation paradigms, increases locus coeruleus activity, and has been associated in epidemiological studies with diminished Alzheimer’s risk and better cognitive function. None of this establishes causation, but it sketches a coherent framework in which chronic, severe sleep inertia could serve as an early behavioral fingerprint of arousal-circuit vulnerability.

The authors are careful to frame the work as exploratory and preliminary. The cross-sectional design cannot determine whether sleep inertia contributes to cognitive decline, shares an underlying cause with it, or both. The sample, while large and well characterized, was predominantly white and non-Hispanic, limiting generalizability, and most cognitive testing occurred in the evening hours—though the models adjusted for hours since awakening, which the researchers argue makes it unlikely that the measured performance simply reflected acute grogginess during testing. The Sleep Inertia Questionnaire also relies on retrospective self-report, and because the analyses were exploratory, no correction for multiple comparisons was applied, leaving open the possibility that some associations reflect chance. Objective, ambulatory measures of sleep inertia and longitudinal designs in more diverse populations are the clear next steps.

Even with those caveats, the implications are tangible. Sleep inertia is a near-universal experience that typically dissipates within 30 minutes of waking, but its duration and intensity vary dramatically between individuals, and prolonged, debilitating inertia is a hallmark of idiopathic hypersomnia and a frequent complaint in obstructive sleep apnea, insufficient sleep syndrome, circadian rhythm disorders, and mood disorders. If replicated, the current findings suggest that a simple question about morning grogginess—how long it lasts, how severe it feels, how often it occurs—could be folded into clinical assessments of older adults as an inexpensive, readily obtainable signal of cognitive risk. They also raise the possibility that interventions designed to reduce sleep inertia, from optimized wake timing to pharmacological countermeasures, might one day help modify cognitive trajectories in aging. For now, the message is that the fog of waking up may be more than a nuisance: it could be a window into how the aging brain transitions between states of consciousness, and how well it will continue to perform.

Subject of Research: The association between sleep inertia severity and cognitive performance in older adults

Article Title: Association between sleep inertia and cognitive performance among older adults in the Wisconsin Sleep Cohort study

Article References: Love, J. J., Cook, J. D., Hagen, E. W., Rasmunon, A., Ravelo, L. A., Palta, M., Peppard, P. E., & Plante, D. T. (2026). Association between sleep inertia and cognitive performance among older adults in the Wisconsin Sleep Cohort study. Journal of Clinical Sleep Medicine, 22(1), Article 105. https://doi.org/10.1007/s44470-026-00133-4

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00133-4

Keywords: sleep inertia, cognitive performance, older adults, Wisconsin Sleep Cohort, hypersomnolence, executive function, psychomotor speed, neurocognitive testing, locus coeruleus, Alzheimer's disease, daytime sleepiness, sleep health

The original study, published in the Journal of Clinical Sleep Medicine, followed a 25-year propensity-matched cohort of veterans with narcolepsy and compared their outcomes against patients treated at general sleep clinics, most of whom carried diagnoses of obstructive sleep apnea. The findings raised eyebrows because the excess mortality appeared concentrated in patients classified under an “other narcolepsy” category rather than in those with narcolepsy type 1, the classic form of the disease characterized by cataplexy and low cerebrospinal fluid levels of the wakefulness neuropeptide hypocretin. This inversion of expectations prompted Fei and colleagues to ask whether the “other narcolepsy” group was simply a diagnostic dumping ground, a mixture of narcolepsy type 2, unspecified narcolepsy, and misclassified hypersomnolence conditions whose apparent mortality excess might be an artifact of sloppy phenotyping rather than a genuine biological signal.

In their reply, Sharafkhaneh and his coauthors, an international roster that includes Yves Dauvilliers of Montpellier, Ahmed BaHammam of King Saud University, Michael Thorpy of Albert Einstein College of Medicine, Fang Han of Peking University, and Turkish sleep specialists Gulcin Benbir Senel and Murat Aksu, do not dispute that heterogeneity exists. Instead, they argue that it is an unavoidable and explicitly acknowledged feature of real-world electronic health record phenotyping. The researchers intentionally defined the other narcolepsy group as a heterogeneous category, and they point out that their design required at least two diagnostic codes recorded 30 to 390 days apart before a patient entered the cohort. That temporal spacing requirement, they explain, was engineered to improve diagnostic specificity by filtering out transient miscoding and diagnostic uncertainty, the kind of single-visit noise that plagues administrative claims data.

The technical crux of the debate revolves around the multiple sleep latency test, or MSLT, the gold-standard physiological instrument for distinguishing narcolepsy type 1 from narcolepsy type 2 and idiopathic hypersomnia. The test measures how quickly patients fall asleep during scheduled daytime naps and whether they enter rapid eye movement sleep abnormally early, a phenomenon known as sleep-onset REM periods. Fei and colleagues highlighted the well-documented poor test-retest reliability of the MSLT in noncataplectic hypersomnia, citing influential work by Lynn Trotti and colleagues showing that patients can flip between diagnostic categories when the test is repeated. The reply authors acknowledge this limitation candidly but note a practical reality: MSLT data were simply not available in a standardized manner across the national Veterans Health Administration dataset, making physiological confirmation impossible for a cohort of this scale.

Rather than abandon physiological rigor altogether, the investigators constructed their cohort using a validated computable phenotyping algorithm analogous to the approach applied by Mayo Clinic researchers in prior narcolepsy cohort studies. This algorithmic strategy, the authors argue, does not eliminate misclassification, but it meaningfully minimizes the single-code phenotyping errors that arise when researchers rely on any one diagnostic entry in isolation. The distinction matters because the entire interpretive weight of the mortality finding depends on what the other narcolepsy group actually contains. If the group is a genuine mixture of undertreated or atypically presenting hypersomnolence patients, the authors contend, one would expect mortality rates equal to or higher than those seen in narcolepsy type 1, precisely the pattern the data revealed. Crucially, they emphasize, their findings highlight this heterogeneity but do not imply that narcolepsy type 2 itself carries an inherently elevated mortality risk.

Fei and colleagues also raised the possibility that positive airway pressure therapy, widely prescribed for obstructive sleep apnea, might have artificially lowered mortality in the general sleep clinic comparator group, citing a recent meta-analysis by Benjafield and colleagues suggesting that PAP use is associated with reduced all-cause and cardiovascular mortality. The reply authors treat this as a plausible but ultimately insufficient explanation. Their data showed obstructive sleep apnea prevalence of 65.4 percent in the general sleep clinic cohort compared with only 33 to 37 percent in the two narcolepsy groups, yet differential PAP exposure cannot explain why mortality differed between the other narcolepsy group and narcolepsy type 1, since neither group received PAP at rates approaching the comparator. The confounding argument, in other words, addresses the wrong contrast.

Perhaps the most striking observation in the exchange concerns comorbidities. The general sleep clinic cohort carried a substantially heavier baseline burden of cardiovascular, metabolic, pulmonary, and psychiatric disease than either narcolepsy group, yet the elevated mortality in narcolepsy persisted despite this seemingly protective asymmetry. If comorbidity profiles alone drove the mortality differences, the general sleep clinic patients should have fared worse. The authors are careful to stress that their study cannot identify causal mechanisms; what it delivers is a significant association that they argue warrants deeper investigation in cohorts enriched with physiologic data and treatment-adherence information, ideally including device-level PAP downloads and pharmacy fill trajectories that would allow researchers to model therapeutic exposure directly.

Treatment exposure emerges as a genuinely promising mechanistic thread in the reply. In the real-world VA data, patients with narcolepsy type 1 received sodium oxybate, stimulants, and wake-promoting agents more frequently than the other narcolepsy group, a pattern the authors attribute to greater diagnostic certainty and the established therapeutic indication for cataplexy rather than to disparities in clinical access. Fei and colleagues noted that low-sodium oxybate formulations are associated with a more favorable cardiovascular profile, referencing a 2025 study in Hypertension comparing the blood pressure effects of high- versus low-sodium oxybate in narcolepsy patients. The reply authors agree that differential pharmacologic exposure deserves further research, and they go further, suggesting that unequal treatment exposure may itself represent an important mechanism contributing to poorer outcomes in the heterogeneously composed other narcolepsy group, a hypothesis with direct implications for how clinicians manage patients whose diagnoses remain uncertain.

The broader literature on narcolepsy mortality remains stubbornly inconsistent, and the reply situates the debate within that larger landscape. A recent Taiwanese cohort study by Hsu and colleagues, published in JAMA Network Open, reported no excess all-cause or cause-specific mortality among narcolepsy patients after adjustment for comorbidities, standing in apparent contradiction to the VA findings of elevated mortality relative to a clinically complex sleep-clinic comparator. Reconciling these divergent results, the authors argue, will require harmonized phenotyping standards, cause-specific death ascertainment, and treatment-exposure modeling across diverse health systems, since a diagnosis of narcolepsy may carry different meanings in a national claims database, a single-payer registry, and an integrated veteran health system. The authors express hope that their work, together with the constructive critique from Fei and colleagues, will catalyze such multi-system efforts.

What makes this exchange more than an academic squabble is what it reveals about the epistemology of modern sleep medicine. As ever-larger electronic health record datasets become the primary engine of epidemiological discovery, researchers must navigate a fundamental tension between scale and precision: cohorts large enough to detect mortality signals over decades are rarely cohorts in which every diagnosis has been physiologically confirmed. The VA team’s reply models one way of handling that tension, defending algorithmic phenotyping while transparently cataloging its limits and inviting the very scrutiny it received. For patients with narcolepsy type 2 and related hypersomnolence disorders, the practical message is nuanced but important: the apparent mortality excess in non-type 1 narcolepsy may reflect diagnostic mixture and undertreatment rather than the disease itself, which means that clearer diagnostic pathways and more aggressive therapeutic engagement for diagnostically ambiguous patients could be among the most consequential interventions sleep medicine can offer.

Subject of Research: Mortality risk and diagnostic heterogeneity in narcolepsy type 2 and other hypersomnolence disorders using electronic health record phenotyping

Article Title: Reply to “Unexpectedly higher mortality in non‑type 1 narcolepsy: diagnostic heterogeneity or unmeasured confounders?”

Article References: Sharafkhaneh, A., Dauvilliers, Y., BaHammam, A. S., Azarian, M., Thorpy, M., Han, F., Senel, G. B., Aksu, M., & Razjouyan, J. (2026). Reply to “Unexpectedly higher mortality in non‑type 1 narcolepsy: diagnostic heterogeneity or unmeasured confounders?”. Journal of Clinical Sleep Medicine, 22(1), Article 124. https://doi.org/10.1007/s44470-026-00120-9

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00120-9

Keywords: narcolepsy, narcolepsy type 1, narcolepsy type 2, mortality, electronic health records, multiple sleep latency test, sodium oxybate, obstructive sleep apnea, positive airway pressure, hypersomnolence, sleep medicine, Veterans Affairs cohort