The core of the critique concerns a statistical paradox surrounding insomnia severity. Baron and colleagues built part of their interpretation on the idea of hyperarousal, a physiological state in which the brain and body remain unusually activated during sleep. Under this framework, people with insomnia symptoms may genuinely experience sleep as wakefulness, perceiving themselves as lying awake when, objectively, they have been asleep. It is an influential concept in sleep medicine, and it offers an elegant explanation for why subjective and objective measures of sleep so often diverge. Yet Khan and Riaz point out that the study’s own numbers appear to undercut this narrative. In the partially adjusted model reported in the paper’s Table 2, the Insomnia Severity Index, a standard questionnaire measure abbreviated as ISI, was a statistically significant predictor of sleep underestimation, with a beta coefficient of negative 0.0497 and a p value of 0.0077. In the fully adjusted subjective analysis, however, that significance vanished entirely, leaving a p value of 0.1503.

That shift matters because of what statistical adjustment actually does. Partially adjusted models account for only a limited set of confounding variables, while fully adjusted models attempt to isolate the independent contribution of each predictor by controlling for everything else measured in the study. When a variable that appears significant in a simpler model loses its significance once broader factors are included, it suggests that the variable’s apparent effect may have been an artifact of its relationship with those other factors. In this case, Khan and Riaz argue, the loss of significance for the Insomnia Severity Index implies that subclinical insomnia does not act independently as a cause of sleep misperception once global stress and overall sleep difficulties are taken into account. If that is true, the authors of the letter contend, the hyperarousal interpretation should be treated with caution rather than folded into the study’s finalized conclusions as though it had been confirmed.

The second major criticism strikes at something even more fundamental: the reliability of actigraphy itself. Actigraphy devices, typically worn on the wrist, use accelerometers to detect movement and apply algorithms to infer sleep and wakefulness from motion patterns. The assumption is simple enough. When the wearer is still, the device scores sleep; when the wearer moves, the device scores wakefulness. Baron and colleagues were candid about the technology’s limits, acknowledging in their own paper that actigraphy does not serve as ground truth and that it tends to overestimate sleep duration relative to polysomnography, the gold-standard laboratory method that records brain waves, eye movements, and muscle tone. Polysomnography can distinguish light sleep, deep sleep, rapid eye movement sleep, and wakefulness with far greater precision, but it is expensive, intrusive, and impractical for extended home monitoring, which is precisely why actigraphy remains so widely used.

The problem, as Khan and Riaz frame it, is a contradiction between what the original authors admitted and what they then did. If actigraphy systematically overestimates sleep, using its output as a fixed benchmark against which subjective reports are judged becomes questionable. The discrepancy between a person’s self-reported sleep and the device’s estimate could reflect genuine misperception on the part of the sleeper, or it could reflect measurement error on the part of the device. Without a more accurate reference standard, the study cannot cleanly separate the two. The letter’s authors argue that this limitation is especially consequential in the specific cohort under study, which consisted of highly stressed individuals with elevated blood pressure, conditions associated with physiological hyperarousal.

Why does hyperarousal make actigraphy particularly unreliable in this population? The answer lies in what sleep scientists sometimes call quiet or silent wakefulness. A person lying motionless in bed, mind racing but body still, produces almost no accelerometer signal. To the device, that stillness is indistinguishable from sleep. In a stressed, hypertensive cohort, the letter suggests, such silent wakefulness may be common, meaning the actigraph could be scoring substantial periods of wakefulness as sleep. If that happened, the so-called objective measure would inflate sleep duration, and the apparent underestimation by participants, who reported sleeping less than the device recorded, could actually be an artifact of device overestimation rather than a genuine perceptual error. The direction of the discrepancy, in other words, might be reversed.

Khan and Riaz raise a further technical question about how actigraphy algorithms handle the boundary between sleep and quiet wakefulness. Whether a given device and scoring algorithm records complete sleep and silent wakefulness as discrete categories, or instead merges them into a single undifferentiated signal, determines whether the resulting data can support the study’s conclusions at all. If the algorithm merges the two states, the objective estimates become unreliable for assessing misperception, because the benchmark itself is contaminated. This is not a hypothetical concern specific to one study; it is a structural weakness of movement-based sleep measurement that becomes acute precisely in the populations where sleep misperception is most likely to occur, namely people with stress, insomnia symptoms, and heightened physiological arousal.

The exchange highlights a broader tension in contemporary sleep science, one that has grown more visible as consumer wearables have flooded the market. Millions of people now check nightly sleep scores on their wrists, treating the numbers as objective fact. But the same limitations that complicate research-grade actigraphy apply to consumer devices, often with less validation. Studies comparing wearable estimates to polysomnography have repeatedly found that these devices tend to overestimate total sleep time, particularly in people with fragmented or disturbed sleep. When researchers then use such devices to study the gap between perceived and actual sleep, they risk building interpretations on a foundation that shifts depending on who is wearing the device and under what conditions. The letter by Khan and Riaz is a reminder that the word objective, when attached to a measurement, does not automatically mean accurate.

What would a more rigorous approach look like? The letter does not propose a full redesign, but its logic points toward several priorities. Studies of sleep misperception in hyperaroused populations would benefit from polysomnographic confirmation, at least in a subsample, to calibrate the actigraphic estimates against a standard that can actually distinguish silent wakefulness from sleep. Statistical modeling should present fully adjusted results as the primary basis for interpretation, rather than allowing partially adjusted findings to drive conceptual claims about mechanisms like hyperarousal. And researchers should be explicit about the direction of potential measurement bias, considering device overestimation as an alternative explanation rather than defaulting to narratives about patient misperception. None of this diminishes the value of the original study’s contribution in drawing attention to the discrepancy between subjective and objective sleep assessment in short sleepers, but it does suggest that the field’s confidence in actigraphy-based conclusions should be calibrated to the technology’s real limits.

For now, the letter stands as a concise but pointed methodological intervention, published as Volume 22, article number 112 of the Journal of Clinical Sleep Medicine, with no external funding and no declared competing interests. Its authors conceptualized the critique together, with Khan drafting the initial manuscript and Riaz revising it for intellectual content. Whether Baron and colleagues respond in kind remains to be seen, but the substance of the critique is likely to resonate beyond this single exchange. Sleep misperception is a phenomenon with real clinical consequences, shaping decisions about treatment, driving worry in people who believe they are chronically sleep-deprived, and informing public health messaging about sleep duration. If the instruments used to define objective sleep systematically blur the line between sleeping and lying quietly awake, then both researchers and the public would do well to treat wrist-based sleep numbers as estimates with known blind spots, not as ground truth. The letter’s central message is ultimately about epistemic humility: before declaring that people misperceive their sleep, science must first be certain that the devices measuring it are not misperceiving in their place.

Subject of Research: Methodological limitations of actigraphic sleep measurement and statistical analysis in studies of sleep duration misperception

Article Title: Limitations in study of actigraphic estimates of sleep

Article References: Khan, A. W., & Riaz, A. (2026). Limitations in study of actigraphic estimates of sleep. Journal of Clinical Sleep Medicine, 22(1), Article 112. https://doi.org/10.1007/s44470-026-00132-5

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00132-5

Keywords: actigraphy, sleep duration, sleep misperception, insomnia, hyperarousal, polysomnography, statistical adjustment, wearable devices, sleep medicine, measurement bias, Journal of Clinical Sleep Medicine, silent wakefulness

The stakes of this question are considerable. Disability insurance programs in the United States provide a financial lifeline to millions of workers whose medical conditions prevent them from maintaining substantial employment. An allowance decision can determine whether an individual has access to monthly cash benefits, health insurance coverage through Medicare after a qualifying waiting period, and a measure of economic security during what is often the most vulnerable period of their working life. Because these decisions carry such weight, any systematic difference in how applicants from different racial and ethnic groups fare in the process would raise serious concerns about fairness and equal treatment under the law. That is precisely why researchers have repeatedly examined allowance rates by race and ethnicity, and why unadjusted statistics showing gaps between groups have drawn attention from policymakers and analysts alike.

The methodological approach taken in the study is central to its contribution. Rather than relying on aggregate approval statistics alone, the investigators combined data from the American Community Survey, one of the largest and most detailed ongoing household surveys in the United States, with Social Security administrative records. This linkage allowed the researchers to observe not just whether an applicant was allowed or denied, but a rich set of characteristics describing who that applicant was before the determination was made: their marital status, their health conditions, their earnings history prior to disability, and a range of other demographic and socioeconomic attributes. This kind of linked administrative-survey infrastructure is relatively rare and highly valued in social science research, because it permits analysts to move beyond simple comparisons of group averages and toward a more complete accounting of the factors that actually drive outcomes.

The central analytical question is one that economists describe as a problem of composition versus process. Raw, unadjusted data showed differences in allowance rates across racial and ethnic groups. But a raw gap of that kind can arise in two very different ways. It could indicate that the determination process treats similarly situated applicants differently, which would suggest a disparity within the system. Alternatively, it could reflect the fact that applicants from different groups arrive at the process with different characteristics on average, such as different distributions of impairments, different work histories, different ages, or different household circumstances, and that those differences in applicant composition produce different outcomes even if the process itself is applied uniformly. Distinguishing between these two explanations requires statistical adjustment, and that is exactly what the study provides.

When the researchers accounted for socioeconomic, health, and demographic factors, the picture changed substantially. Most of the observed differences in approval rates became much smaller, and many were no longer statistically distinguishable from zero. In other words, once the researchers compared applicants who were similar in terms of the characteristics that matter for disability determinations, the residual differences associated with race and ethnicity themselves largely disappeared. The authors summarized their finding directly, writing that the differences in allowance rates by race and ethnicity in the raw, unadjusted data are mainly due to compositional differences between groups. This conclusion points away from the determination process as the primary source of the observed gaps and toward the underlying circumstances that shape who applies and with what profile of conditions and resources.

Among the factors the study identifies as associated with allowance outcomes are marital status, health conditions, and pre-disability earnings. Each of these has a plausible connection to how disability claims unfold. Health conditions are, of course, at the heart of any disability determination, since the severity and nature of impairments are what adjudicators assess. Pre-disability earnings matter because they relate to insured status and to the earnings requirements that govern eligibility for disability benefits, as well as to the kinds of work an applicant can realistically be expected to perform in the residual functional capacity framework used in evaluations. Marital status can influence household resources and the support structures available to applicants as they navigate a lengthy and often demanding application process. Differences across racial and ethnic groups in the distributions of these characteristics can therefore translate into differences in approval rates even in the absence of differential treatment within the process itself.

The findings carry important implications for how the raw statistics should be interpreted. A gap in unadjusted allowance rates is often read, in public discussion, as evidence of bias in the system. The study does not support that reading as the primary explanation. At the same time, the results should not be taken to mean that the underlying disparities are unimportant or that no policy concern remains. If applicants from different groups arrive with systematically different health conditions, earnings histories, and household circumstances, those compositional differences themselves reflect broader inequalities in American society, including differences in access to healthcare, in occupational structures, and in economic resources. The study’s contribution is to locate the source of the allowance-rate gaps in these upstream factors rather than in the determination process, which redirects attention toward the social and economic conditions that produce different applicant pools in the first place.

For policymakers, the research offers a more precise map of where intervention might be most effective. If the determination process were the main driver of the gaps, reforms would focus on adjudication standards, training, and oversight of decision-makers. Because the study finds that compositional differences dominate, the more relevant levers may lie in addressing the health and economic disparities that shape disability incidence and application behavior across communities. The findings also illustrate the value of linked administrative data for evaluating social insurance programs. Without the ability to connect survey-based measures of applicant circumstances to actual determination outcomes, analysts would be left with unadjusted statistics that invite misinterpretation. The study demonstrates how such data infrastructure can sharpen the evidence base on which fair and effective policy depends.

The study, published in Contemporary Economic Policy, a journal that has since 1982 provided a forum for scholarly analysis of important policy issues accessible to both economists and policymakers, adds a rigorous and carefully controlled data point to a debate that is often conducted with incomplete evidence. Its central message is methodological as much as substantive: raw differences between groups are not the same as disparities produced by a process, and distinguishing between the two requires data that capture the full circumstances of the people moving through the system. In the case of US disability insurance, the researchers find that when those circumstances are measured and held constant, race and ethnicity themselves largely fall out of the story, leaving marital status, health conditions, and pre-disability earnings as the factors that travel with applicants into the determination process and shape the outcomes they receive.

Subject of Research: Racial and ethnic differences in US disability insurance allowance rates and their socioeconomic and health-related explanations

Article Title: Why are there differences in US disability insurance allowance rates by race and ethnicity?

Article References: Why are there differences in US disability insurance allowance rates by race and ethnicity?. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: disability insurance, Social Security, allowance rates, race and ethnicity, American Community Survey, administrative data, health conditions, pre-disability earnings, marital status, Contemporary Economic Policy, social insurance policy, statistical adjustment