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

Abstract

Study Objectives: Emerging evidence supports a multidimensional perspective of sleep in the context of health. The sleep health model, and composite sleep health score, are increasingly used in research. However, specific cutoff values that differentiate "good" from "poor" sleep, have not been empirically derived and its relationship to cardiometabolic health is less-well understood. We empirically derived cutoff values for sleep health dimensions and examined the relationship between sleep health and cardiometabolic morbidity.Methods: Participants from two independent Biomarker Studies in the MIDUS II (N = 432, 39.8% male, age = 56.92 ± 11.45) and MIDUS Refresher (N = 268, 43.7% male, age = 51.68 ± 12.70) cohorts completed a 1-week study where sleep was assessed with daily diaries and wrist actigraphy. Self-reported physician diagnoses, medication use, and blood values were used to calculate total cardiometabolic morbidity. Receiver operating characteristic (ROC) curves were generated in the MIDUS II cohort for each sleep health dimension to determine cutoff values. Using derived cutoff values, logistic regression was used to examine the relationship between sleep health scores and cardiometabolic morbidity in the MIDUS Refresher cohort, controlling for traditional risk factors.Results: Empirically derived sleep health cutoff values aligned reasonably well to cutoff values previously published in the sleep health literature and remained robust across physical and mental health outcomes. Better sleep health was significantly associated with a lower odds of cardiometabolic morbidity (OR [95% CI] = 0.901 [0.814-0.997], p = .044). Conclusions:These results contribute to the ongoing development of the sleep health model and add to the emerging research supporting a multidimensional perspective of sleep and health. Statement of SignificanceDespite the increasing use of the sleep health model in research, cutoff values that differentiate "good" from "poor" sleep have not been empirically derived and its relationship to cardiometabolic health is less-well understood. We empirically derived cutoff values for each sleep health dimension and examined whether sleep health related to cardiometabolic morbidity. Empirically derived sleep health cutoff values reasonably aligned to cutoff values previously published in the sleep health literature and were robust across mental and physical health outcomes. Poor sleep health was significantly associated with a greater prevalence of cardiometabolic morbidity. These results contribute to the development of the sleep health model and add supportive evidence for a multidimensional perspective of sleep.

Sleeping when the world locks down: Correlates of sleep health during the COVID-19 pandemic across 59 countries

“…Poor sleep is associated with adverse health outcomes, including cardiovascular disease, 2 cancer, 3 hypertension, 4 obesity & diabetes, 5 and all-cause mortality, 6 as well as psychological disorders such as depression. 7 Therefore, maintaining sleep health may be critical to preserving good overall physical and mental health during the COVID-19 pandemic.…”

Effect of a physical activity and sleep m-health intervention on a composite activity-sleep behaviour score and mental health: a mediation analysis of two randomised controlled trials

“…Consistent with the aims of the study, an activity–sleep index (ASI) was created to summarise the multiple dimensions of physical activity and sleep health. The ASI included 12 dimensions: six physical activity dimensions based on the frequency, duration, intensity and type of physical activity, and six sleep health dimensions based on the definition of sleep health [ 11 , 39 – 41 ]. The specific items, responses, and scoring for the ASI and provided in Supplementary Table 2 .…”

Multidimensional Sleep Health: Concepts, Advances, and Implications for Research and Intervention

“…As described in (14), for the summary score (21)(22)(23), cut-points defined optimal ranges, with coding of 'favorable' or 'healthier' sleep as '1' and non-optimal ranges as '0'. Optimal ranges were drawn from the literature, the NSF's objective sleep quality report (eg, for WASO), expert consensus, or sample characteristics (12,13,18,19,21).…”

“…For public health utility, however, a categorical basis using prior knowledge to define optimal ranges for each sleep dimension may also be appropriate. Early work operationalized Ru SATED domains by dichotomizing daily diary, questionnaire, or actigraphy measures and summing them into an overall sleep score (21)(22)(23). While continuous sleep exposures or outcomes offer greater statistical power, categorical assessmentsthose involving a cut-pointprovide three practical benefits (30).…”

“…spindles, k-complexes) has yet to be established despite emerging data on the unique information contained in these measures (27,46). These limitations highlight that sleep health is undergoing conceptual advances and scale development (1, 6), scale validation (5,6), implementations of sleep health in cohort studies and community samples (21,47), innovations in methodological approaches (4,7,41,48), optimal range and threshold determination (18,22), and many practical choices on how best to analyze multidimensional sleep data. We suggest that statistical dependencies are an additional factor to contend with.…”