Journal of neurophysiologyOct 26, 2026

Sleep Measures in Health Studies: From Being Left Out to Being Included - A Review and Suggestion

Circadian Biology Weekly Brief ↗PubMed ↗DOI ↗

Updated Oct 27, 2026

Abstract

Omitting sleep metrics in health research may create significant residual confounding.

Current health research primarily relies on demographics, anthropometrics, and socioeconomic status for sample characterization, neglecting sleep as a critical variable.
Sleep influences several biological processes, including hormone regulation, immune function, and metabolic clearance, which are essential for maintaining homeostasis.
Research indicates that sleep duration, quality, and chronotype can affect insulin sensitivity, hormonal balance, and cardiovascular health.
Not accounting for sleep metrics may result in misclassifying participants as 'non-responders' in clinical trials, obscuring the effects of interventions.
A proposed Minimal Sleep Set (MSS) aims to systematically control for sleep-wake variance using validated screening tools to improve study accuracy.

Simplified

For decades, sample characterization in health and physiological research has relied on a "triad of convenience": demographics, anthropometrics, and socioeconomic status. While these parameters offer a baseline description, they systematically omit a critical biological variable sustaining human homeostasis: sleep. This baseline omission introduces significant residual confounding, creating a "Table 1 Fallacy" where matched cohorts appear identical but remain physiologically heterogeneous due to unmeasured sleep-wake variance, including duration, quality, and circadian alignment. Sleep occupies a unique position among neglected baseline variables because it simultaneously coordinates neuroendocrine secretion, immune priming, autonomic regulation, and metabolic clearance within each circadian cycle, mechanistic roles that differentiate it from other lifestyle factors currently absent from reporting guidelines. Compelling evidence demonstrates that sleep duration, quality, and chronotype directly modulate peripheral insulin sensitivity, hormonal balance, immune readiness, and cardiovascular function. Failing to account for these metrics can lead to the misclassification of participants as "non-responders" in clinical trials, masking true primary intervention effects or creating an illusion of treatment efficacy. To restore reproducibility and scientific rigor, we contend that sleep reporting must transition from a voluntary act to a methodological mandate in studies where sleep represents a biologically plausible source of confounding. We propose a high-yield, low-cost Minimal Sleep Set (MSS) utilizing validated screening tools to systematically control for sleep-wake variance, ensuring that the human biological state is as clearly defined as the intervention itself.

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Sleep Measures in Health Studies: From Being Left Out to Being Included - A Review and Suggestion

Abstract

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