SLEEP Editor’s Choice: How Empirically Differentiating “Good” from “Poor” Sleep Relates to Cardiometabolic Health
The paper described below was selected as a SLEEP Editor’s Choice.
Sleep can play an important role in overall mental and physical health. A multidimensional sleep health score assesses sleep through six dimensions: sleep duration, timing, regularity, efficiency and quality, and daytime alertness. Although this score helps scientists create a nuanced sleep health profile, which is critical to analyzing sleep quality in relation to mental and physical health outcomes, empirical measures of what constitutes “good” and “poor” sleep, across each of these dimensions, has so far been missing from the literature.
In a new study published in SLEEP, researchers including Ryan C. Brindle, PhD (former Pitt Psychiatry postdoctoral fellow; current Assistant Professor of Cognitive and Behavioral Science, Washington and Lee University) and Martica Hall, PhD from Pitt Psychiatry (Professor of Psychiatry, Psychology, and Clinical and Translational Science), analyzed data from MIDUS II and MIDUS Refresher Biomarker studies to empirically derive cut-off values of all six sleep dimensions. Further, they examined the degree to which sleep health is associated with cardiovascular diseases and metabolic disorders, and evaluated whether the sleep health metric outperformed other, more commonly used epidemiological measures.
Data for the current study were collected from 700 MIDUS II and MIDUS Refresher Biomarker study participants, who wore wrist actigraphs, and completed a sleep diary for seven consecutive days. Sleep health dimension data from the MIDUS II participants informed the development of sleep health score cut-off values, which were then used to analyze the relationship between sleep health scores and cardiometabolic morbidity in participants from the MIDUS Refresher study. Cardiometabolic outcomes were calculated based on current medication use, blood panel values and self-reported physician diagnoses of heart disease, hypertension, stroke and diabetes. The six dimensions of sleep health were derived from self-reported information (sleep quality and daytime alertness) and the actigraphy data (sleep duration, timing, regularity, and efficiency).
The investigators found that the cut-off values that emerged from their analysis were reasonably aligned with cut-off values already reported in previous literature, although the value for sleep duration was notably shorter than in earlier studies. In addition, the research team confirmed their hypothesis that better sleep is significantly associated with reduced odds of reporting cardiometabolic morbidity and hypertension, even after controlling for sociodemographic and biological risk factors.
Regarding these findings, Dr. Hall, the study’s senior author, remarked, “Dr. Brindle’s analyses of the MIDUS data go a long way towards establishing that health is best predicted by multidimensional measures of sleep. These data quite clearly show that sleep duration alone doesn’t tell the whole story. Among other factors, when we think about the importance of sleep to health, we need to consider when, how soundly, and how regularly we sleep.”
Empirical derivation of cutoff values for the sleep health metric and its relationship to cardiometabolic morbidity: Results from the Midlife in the United States (MIDUS) study
Brindle RC, Yu L, Buysse DJ, Hall MH
Sleep, Volume 42, Issue 9, September 2019, zsz116, https://doi.org/10.1093/sleep/zsz116