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 study under examination, conducted by Aderman and colleagues within the CARE Consortium, examined the association between sleep duration and computerized neurocognitive testing outcomes, both at baseline and after injury, across cadets and midshipmen at US service academies. Large consortium datasets of this kind are among the most valuable resources in sleep and military health research, because they aggregate thousands of observations that no single laboratory could collect. Yet Saripudin’s letter argues that the very scale and structure of such datasets introduce interpretive hazards that can quietly distort the conclusions drawn from them, particularly when psychological burden is treated as a background variable rather than an active player in the sleep-cognition relationship.

The core of the argument rests on a well-established but frequently overlooked point: sleep duration and psychological distress are deeply entangled. Cadets operate under sustained academic, physical and social stressors, and the psychological load they carry is not merely a consequence of short sleep but also a determinant of it. Anxiety and depressive symptoms can shorten sleep, fragment it, and simultaneously degrade the very cognitive functions that computerized neurocognitive batteries are designed to measure. If a study models sleep duration as a predictor of cognitive performance without adequately accounting for this psychological burden, the estimated effect of sleep may absorb influences that actually belong to stress, mood, or motivation. Saripudin’s letter suggests that this is precisely the kind of interpretive consideration that deserves greater weight when the CARE Consortium findings are cited.

There is a rich theoretical backdrop for this concern. Attentional control theory, articulated by Eysenck and colleagues in 2007, proposes that anxiety impairs performance primarily by disrupting the efficiency of the executive attention system: anxious individuals can often maintain accuracy on cognitive tasks, but only at the cost of slower responses and greater mental effort. In a cadet population, where subclinical anxiety is common and often adaptive in the short term, this means that two individuals with identical sleep durations may perform very differently on a neurocognitive test depending on their current psychological state. A statistical model that omits this dimension risks attributing to sleep what is, in part, an anxiety effect, or vice versa. The letter’s insistence on separating these constructs is not pedantry; it changes what the results mean for real people.

The military context sharpens the stakes. Earlier work by Lieberman and colleagues demonstrated that severe decrements in cognitive function and mood emerge when sleep loss is combined with heat, dehydration and undernutrition during simulated combat, showing that operational environments stack multiple stressors on top of one another. Cadets in training live inside something resembling this stack: restricted sleep, physical exertion, caloric demands, academic pressure and the constant evaluation culture of service academies. Under such conditions, a measured association between sleep duration and test scores is a composite signal. It may reflect genuine neurobiological effects of sleep on memory consolidation and executive function, but it may also reflect fatigue, emotional strain, or the motivational state of a cadet sitting down to a computerized test after a hard day of training.

Saripudin’s letter also engages with broader neuroscience evidence on why sleep duration matters for cognition in the first place. A 2022 study by Tai, Chen, Manohar and Husain in Communications Biology reported that sleep duration relates both to executive function and to brain structure, providing a plausible biological substrate for the associations observed in cadet samples. Findings like these make it tempting to read every correlation between short sleep and poorer test performance as evidence of structural or functional brain consequences. The letter’s caution is that such readings require the psychological confounds to be ruled out first, because the same behavioral outcome can arise from very different underlying processes, only some of which involve the brain changes that sleep researchers care about.

Another layer of the argument concerns the measurement instruments themselves. Computerized neurocognitive batteries, of the kind used at baseline and post-injury in the CARE Consortium studies, are sensitive to practice effects, testing conditions, and the effort that examinees invest. In military settings, where testing can carry career consequences, motivation to perform is not constant across individuals or occasions. A cadet who is sleep-deprived and psychologically burdened may approach a baseline test differently from a rested, unstressed peer, and these differences in test-taking behavior can masquerade as cognitive deficits. The letter’s methodological point is that interpreting group differences in test scores as direct readouts of cognitive capacity requires assumptions about measurement invariance that are rarely stated explicitly, let alone tested.

The post-injury dimension of the original study adds further complexity. When neurocognitive testing follows a concussion or other injury, the baseline comparison becomes clinically consequential: return-to-play and return-to-duty decisions can hinge on whether a cadet’s post-injury score deviates from their own baseline. If sleep duration and psychological burden influence baseline scores, then they also influence the reference point against which recovery is judged. A cadet whose baseline was depressed by chronic short sleep and high stress might show a smaller change after injury, not because the injury was milder, but because the starting line was drawn lower. Saripudin’s letter highlights this interpretive chain as a reason to treat sleep and psychological variables not as statistical nuisances but as factors that shape the clinical meaning of the entire testing framework.

What makes the letter noteworthy for the wider scientific conversation is its insistence that cognitive resilience to psychological stress, a topic explored by Flood and Keegan in a 2022 review of military personnel, should be integrated into how sleep-cognition studies are designed and interpreted. Resilience is not a fixed trait distributed evenly across a population; it emerges from the interaction of sleep, stress load, coping resources and cognitive reserve. A consortium dataset that captures sleep duration in hours but leaves psychological burden under-specified can describe patterns without explaining them. The letter argues, in effect, for a more psychologically informed reading of the existing findings, one in which the cadet is understood as a whole person under load rather than as a data point with a sleep statistic attached.

None of this diminishes the value of the original research. Large-scale studies of service academy populations remain essential for understanding how sleep shapes performance in demanding, safety-critical occupations, and the CARE Consortium’s infrastructure is precisely the kind of investment the field needs. The letter’s contribution is to sharpen the questions that such data can legitimately answer, and to remind researchers, clinicians and science journalists alike that a correlation reported in a table is not the same as a mechanism established in a mind. For the cadets whose sleep and cognition are being measured, the practical implications are real: better models that account for psychological burden could eventually lead to better screening, better interpretation of post-injury tests, and better guidance on how much sleep is enough when both minds and missions are on the line. Until then, Saripudin’s methodological caution stands as a useful corrective to the temptation of simple headlines about sleep hours and brainpower.

Subject of Research: Methodological evaluation of sleep duration, psychological burden and cognition in military cadets

Article Title: Evaluating sleep duration, psychological burden, and cognition in cadets: methodological and interpretive considerations

Article References: Saripudin, M. (2026). Evaluating sleep duration, psychological burden, and cognition in cadets: methodological and interpretive considerations. Journal of Clinical Sleep Medicine, 22(1), Article 123. https://doi.org/10.1007/s44470-026-00140-5

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00140-5

Keywords: sleep duration, cognition, military cadets, psychological burden, neurocognitive testing, CARE Consortium, attentional control theory, concussion, military psychology, sleep deprivation, executive function, methodology

The core problem the authors identify is deceptively simple. Classical test theory, the statistical backbone of most reliability work in psychology, assumes that an observed score is the sum of a stable true score and random error. Four assumptions underpin the resulting reliability coefficients: true score and error must be additive, independent, and random, and the true score must not change between repeated measurements. In practice, these assumptions are routinely violated by practice and habituation effects — the well-documented tendency of participants to improve on cognitive and coordinative tasks simply by repeating them, without any intervention in between. When the retest score is systematically better than the test score, the parallel-readings assumption collapses, and with it the interpretability of the reliability coefficient.

The scale of the problem is striking. Habituation effects have been documented across a wide range of standard instruments, including the Stroop test, the Eriksen flanker task, the Simon task, the Digit Symbol Substitution Test, the Wechsler Memory Scale, and the Halstead-Reitan neuropsychological battery. The largest gains typically appear between the first two testing sessions, with progressively flatter improvement curves as participants grow familiar with the task. Long-term learning can even persist for years: performance on the Wechsler Adult Intelligence Scale and the Wechsler Memory Scale has been shown to be affected by task exposure that occurred months or years earlier. In one series of experiments cited in the paper, trial-to-trial random errors in neurocognitive tests started as high as 6 to 43 percent and only stabilized below 5 percent in reaction tasks after several days of repeated testing.

What makes this especially dangerous is that the field’s favorite reliability statistics can look excellent while hiding serious problems. The intraclass correlation coefficient (ICC) and Cronbach’s alpha summarize the ratio of true-score variance to observed variance, but they cannot distinguish between different sources of measurement error. The authors point to published examples where an ICC of 0.9 or higher — conventionally rated as excellent — coexisted with a mean absolute test–retest error of 20 percent and maximum individual errors approaching 90 percent. A high correlation between two measurements says nothing about how close those measurements actually are to each other on the original scale, which is what clinicians and experimenters usually need to know.

The classical remedy for this blind spot is the Bland–Altman analysis, introduced in 1986, which plots the difference between two measurements against their mean. The average difference reveals systematic bias, while the 95 percent limits of agreement — the mean difference plus or minus 1.96 standard deviations — describe the expected range of random scatter. But the authors argue that Bland–Altman analyses, as commonly used, suffer from a critical limitation: the limits of agreement are purely descriptive. They describe where the data fell, but they say nothing about whether that amount of error is acceptable for the scientific or clinical question at hand. Bland and Altman themselves noted in the original 1986 paper that acceptable limits should ideally be defined in advance to aid interpretation — advice that has largely been ignored in practice.

There is a second, subtler flaw. Standard limits of agreement are drawn as parallel horizontal lines on the Bland–Altman plot, which implicitly assumes that the absolute size of measurement error is constant across the whole range of measured values. In many empirical domains, however, the scatter grows as the measured value grows — a phenomenon known as heteroscedasticity. Parallel limits therefore tolerate a disproportionately large percentage error for small measurement values while being overly strict for large ones. A limit of ±1 degree might be trivial for a mean of 180 degrees but catastrophic for a mean of 0.5 degrees, as an example from visual-vertical perception research in stroke patients illustrates.

The new R application addresses both problems with a concept the authors call limits of acceptance, or LoAcc. Unlike limits of agreement, which are computed from the data after the fact, limits of acceptance are defined a priori by the researcher as a proportional threshold — for example, 5 percent of each participant’s mean test–retest value. Because the acceptable band scales with the magnitude of the measurement, the resulting boundaries fan out across the Bland–Altman plot rather than running parallel, correctly accommodating heteroscedastic data. A test–retest difference is classified as acceptable only if it falls within the proportional band for that individual. The app then color-codes the results: green if the systematic bias is non-significant and at least 95 percent of observations fall within the acceptance limits, red if either criterion fails.

The software itself is a Shiny app that automates the entire test–retest evaluation workflow. Users upload an Excel file in wide format, with a participant identifier in the first column and measurement pairs in consecutive columns; the app can also be used for validity studies against a gold standard or for inter-rater objectivity analyses. It then computes the full battery of reliability metrics — all six ICC variants with 95 percent confidence intervals, Cronbach’s alpha, the standard error of measurement, the minimal detectable change, the coefficient of variation, the mean absolute error, and the mean absolute percentage error — alongside a paired-samples t-test for systematic bias and publication-ready Bland–Altman plots. Users can set the acceptance threshold per variable pair, specify measurement units, and export results tables and figures automatically. The code is openly available via the Open Science Framework.

To demonstrate the tool, the authors simulated Stroop test data across four consecutive testing days with 100 virtual participants, using a 5 percent acceptance threshold calibrated to the error level observed after four days of habituation in their earlier empirical work. The progression is instructive. Between days 1 and 2, mean performance rose significantly and 86 of 100 observations fell within the acceptance limits — both criteria failed, and the plot lit up red. Between days 2 and 3, the systematic bias had largely vanished, but random scatter remained too high, with 88 of 100 observations inside the limits. Only between days 3 and 4 did the data satisfy the criterion: no significant bias, 97 of 100 observations within the limits, and the mean absolute error shrinking from 0.828 seconds to 0.479 seconds. Applied to a real dataset from a choice reaction task tested twice daily over five days, the app showed the same convergence, and also revealed a near-threshold case at a 10 percent limit where a single data point sitting exactly on the boundary tipped the classification — a reminder, the authors note, that such judgments should consider the magnitude of the deviation rather than being treated as purely binary.

The broader message is a call for transparency that resonates far beyond cognitive psychology. The authors argue that expected intervention-induced changes from previous studies can serve as a rational anchor for setting acceptance limits: a 10 percent measurement error might be tolerable if an intervention is expected to change the outcome by 100 percent, but it is unacceptable if the expected effect is only 10 percent. They acknowledge that no universal guideline exists for what counts as acceptable, and that the 5 and 10 percent thresholds used in their examples are illustrative rather than prescriptive. Still, by forcing researchers to state their tolerance for error in advance and by separating systematic from random error — since a valid ICC interpretation requires the absence of systematic bias — the tool turns habituation from an invisible confound into a measurable, plottable, and ultimately controllable quantity. For a field increasingly worried about the reproducibility of its measurements, knowing exactly how much habituation is enough may prove to be one of the most practical questions psychology has finally learned to answer.

Subject of Research: Test–retest reliability, habituation effects, and agreement analysis in behavioral and neurocognitive research

Article Title: How much habituation is enough? An R application for ad hoc content-related limits of acceptance in behavioral research

Article References: Warneke, K., Siegel, S. D., Afonso, J., Herbsleb, M., & Wallot, S. (2026). How much habituation is enough? An R application for ad hoc content-related limits of acceptance in behavioral research. Behavior Research Methods, 58(11), Article 307. https://doi.org/10.3758/s13428-026-03170-2

Image Credits: AI Generated

DOI: 10.3758/s13428-026-03170-2

Keywords: habituation, practice effects, test–retest reliability, Bland–Altman analysis, limits of agreement, limits of acceptance, intraclass correlation coefficient, Cronbach's alpha, measurement error, R software, psychometrics, neurocognitive testing