Epidemiology of objectively measured bedtime and chronotype in US adolescents and adults: NHANES 2003–2006

Urbanek, Jacek; Spira, Adam P.; Di, Junrui; Leroux, Andrew; Crainiceanu, Ciprian; Zipunnikov, Vadim

doi:10.1080/07420528.2017.1411359

Cited by 39 publications

(45 citation statements)

References 48 publications

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“…The average weekend sleep duration of 8.59 hours closely matches sleep durations from other studies based on self-reported data (Liu et al 2012;Rutters et al 2014;Fischer et al 2017;Koopman et al 2017) , which commonly overestimates sleep duration compared to objective measures such as actigraphy (Lauderdale et al 2006;Silva et al 2007;Arora et al 2013;Dietch et al 2017) . Well-established trends in both objective and subjective sleep analyses demonstrate whites sleep upwards of 45 minutes longer than blacks (Lauderdale et al 2006;Dietch et al 2017) , and women sleep upwards of 30 minutes longer than men (Lauderdale et al 2006;Liu et al 2012;Dietch et al 2017;Urbanek et al 2017) , which are all consistent with our findings.…”

Section: Discussionsupporting

confidence: 91%

“…These patterns are consistent with expected constraints of working-age adults and altered sleep requirements in the elderly, and have been observed previously (Ohayon et al 2004;Silva et al 2007;Hashizaki et al 2015) . Both sleep midpoint and sleep lag also vary as a function of age, which has been observed in adolescents and young adults (Rutters et al 2014;Hashizaki et al 2015;Fischer et al 2017;Koopman et al 2017;Urbanek et al 2017) . Gender and race did not explain social sleep lag, which may reflect broader cultural norms and chronotype shifts in younger individuals regardless of background.…”

Section: Discussionmentioning

confidence: 92%

“…While controlled studies with objective sleep measures provide the strongest evidence for the consequences of sleep disruption, such as the impact on mental health (Minkel et al 2012) , these associations have also been observed through the use of simple questionnaires (Konttinen et al 2014;Gylen et al 2014;de Souza & Hidalgo 2015;An et al 2015) . In fact, associations of sleep with age, gender, race, and metabolic parameters such as body mass index (BMI) are largely consistent, regardless of how the sleep metrics are acquired (Ohayon et al 2004;Lauderdale et al 2006;Silva et al 2007;Liu et al 2012;Rutters et al 2014;Hashizaki et al 2015;Fischer et al 2017;Dietch et al 2017;Urbanek et al 2017) . Questionnaires such as the Morningness-Eveningness Questionnaire (Horne & Ostberg 1976) and Munich Chronotype Questionnaire (Roenneberg et al 2003) are common approaches to gauge an individual's morning or evening preference (i.e., "chronotype"), and are correlated with underlying physiology, including endogenous temperature cycles (Baehr et al 2000) and dim-light melatonin onset (Kantermann et al 2015) .…”

Section: Introductionmentioning

confidence: 99%

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Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

Rhoades

Bastarache

Denny

et al. 2018

Preprint

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The electronic health record (EHR) contains rich histories of clinical care, but has not traditionally been mined for information related to sleep habits. Here we performed a retrospective EHR study and derived a cohort of 3,652 individuals with self-reported sleep behaviors, documented from visits to the sleep clinic. These individuals were obese (mean body mass index 33.6 kg/m 2 ) and had a high prevalence of sleep apnea (60.5%), however we found sleep behaviors largely concordant with prior prospective cohort studies. In our cohort, average wake time was one hour later and average sleep duration was 40 minutes longer on weekends than on weekdays (p<1·10 -12 ). Sleep duration also varied considerably as a function of age, and tended to be longer in females and in whites. Additionally, through phenome-wide association analyses, we found an association of long weekend sleep with depression, and an unexpectedly large number of associations of long weekday sleep with mental health and neurological disorders (q<0.05). We then sought to replicate previously published genetic associations with morning/evening preference on a subset of our cohort with extant genotyping data (n=555). While those findings did not replicate in our cohort, a polymorphism (rs3754214) in high linkage disequilibrium with a previously published polymorphism near TARS2 was associated with long sleep duration (p<0.01). Collectively, our results highlight the potential of the EHR for uncovering the correlates of human sleep in real-world populations.

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Section: Discussionsupporting

confidence: 91%

Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

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Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

Rhoades

Bastarache

Denny

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…Therefore, for the second sensitivity analysis, valid day was defined as a day with wear time between 10 and 20 hours. The threshold of 20 hours has been previously used to identify “bed-time” periods in NHANES [52]. After exclusion of invalid subject-days from the original samples, 3362 (with 534 deaths) subjects remained and same Cox PH models where re-estimated.…”

Section: Resultsmentioning

confidence: 99%

Patterns of sedentary and active time accumulation are associated with mortality in US adults: The NHANES study

Leroux

Urbanek

et al. 2017

Preprint

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PurposeSedentary behavior has become a public health pandemic and has been associated with a variety of comorbidities including cardiovascular disease, type 2 diabetes, and some cancers. Previous studies have also shown that excessive amount of sedentary behavior is associated with all-cause mortality. However, no studies investigated whether patterns of sedentary and active time accumulation are associated with mortality independently of total sedentary and total active times. This study addresses this question by i) comparing several analytical ways to quantify patterns of both sedentary and active time accumulation through metrics of fragmentation of objectively-measured physical activity and ii) exploring the association of these metrics with all-cause mortality in a nationally representative US sample of elderly adults.MethodsThe accelerometry data of 3400 participants aged 50 to 84 in the National Health and Nutrition Examination Survey 2003-2006 cohorts were analyzed. Ten fragmentation metrics were calculated to quantify the duration of sedentary and active bouts: average bout duration, Gini index, average hazard, between-state transition probability, and the parameter of power law distribution. The association of these fragmentation metrics with all-cause mortality followed through December 31, 2011 was assessed with survey-weighted Cox proportional hazard models.ResultsIn models adjusted for age, sex, race/ethnicity, education, body mass index, common comorbidities, and total sedentary/active time, four fragmentation metrics were associated with lower mortality risk: average active bout duration (HR=0.72 for 1SD increase, 95% CI = 0.590.88), Gini index for active bouts (HR = 0.75, 95% CI = 0.64-0.86), the parameter of power law distribution for sedentary bouts (HR = 0.75, 95% CI = 0.63-0.90), and sedentary-to-active transition probability (HR = 0.77, 95% CI = 0.61-0.96), and four fragmentation metrics were associated with higher mortality risk: the active-to-sedentary transition probability (HR = 1.40, 95% CI=1.23-1.58), the parameter of power law distribution for active bouts (HR = 1.33, 95% CI = 1.16-1.52), average hazard for durations of active bouts (HR = 1.32, 95% CI = 1.18-1.48), and average sedentary bout duration (HR =1.07, 95% CI = 1.01-1.13). After sensitivity analysis, average sedentary bout duration and sedentary-to-active transition probability became insignificant.ConclusionLonger average duration of active bouts, a lower probability of transitioning from active to sedentary behavior, and a higher normalized variability of active bout durations were strongly negatively associated with all-cause mortality independently of total active time. A larger proportion of longer sedentary bouts were positively associated with all-cause mortality independently of total sedentary time. The results also suggested a nonlinear association of average active bout duration with mortality that corresponded to the largest risk increase in subjects with average active bout duration less than 3 minutes.

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“…The dim light melatonin onset (DLMO) is the gold standard objective method of detecting circadian phase in humans 34 . Objectively measured bedtime duration and midpoint of the bedtime have been shown to give useful information of chronotype 35 . However, shortened versions of MEQ have been shown to be reliable and are widely used in epidemiological studies 23,24,36 …”

Section: Discussionmentioning

confidence: 99%

Chronotypes and objectively measured physical activity and sedentary time at midlife

Nauha

Jurvelin

Ala‐Mursula

et al. 2020

Scandinavian Med Sci Sports

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Morning, day, or evening chronotypes differ by the circadian timing of alertness and the preferred timing of sleep. It has been suggested that evening chronotype is associated with low physical activity (PA) and high sedentary time (SED). Our aim was to investigate whether such an association is confirmed by objectively measured PA and SED. In 46‐year follow‐up of the Northern Finland Birth Cohort 1966 study, total PA (MET min/day) and SED (min/day) among 5156 participants were determined using wrist‐worn accelerometers for 14 days. We used the shortened Morningness‐Eveningness Questionnaire to define participants' chronotypes. As covariates, we used self‐reported physical strenuousness of work, health, and demographics, and clinical measures. We used adjusted general linear models (B coefficients with 95% confidence intervals, CI) to analyze how chronotype was related to total PA or SED. As compared to evening chronotype, men with day and morning chronotypes had higher total PA volumes (adjusted B 75.2, 95% CI [8.1, 142.4], P = .028, and 98.6, [30.2, 167.1], P = .005). Men with day and morning chronotypes had less SED (−35.8, [−53.8, 17.8], P < .0001, and − 38.6, [−56.9, −20.2], P < .0001). Among women, morning chronotype was associated with higher total PA (57.8, [10.5, 105.0], P = .017), whereas no association between chronotype and SED emerged. Evening chronotype was associated with low objectively measured PA in both sexes and with high SED in men, even after adjustments for established potential confounders. Chronotype should be considered in PA promotion.

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Epidemiology of objectively measured bedtime and chronotype in US adolescents and adults: NHANES 2003–2006

Cited by 39 publications

References 48 publications

Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

Pulling the covers in electronic health records for an association study with self-reported sleep behaviors

Patterns of sedentary and active time accumulation are associated with mortality in US adults: The NHANES study

Chronotypes and objectively measured physical activity and sedentary time at midlife

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