Chronotypes in the US – Influence of age and sex

Abstract

An individual’s chronotype reflects how the circadian system embeds itself into the 24-h day with rhythms in physiology, cognition and behavior occurring accordingly earlier or later. In view of an increasing number of people working at unusual times and linked health and safety risks, the wide range in human chronotypes may provide opportunities to allow people to work (and sleep) at times that are in synch with their circadian physiology. We aimed at estimating the distribution of chronotypes in the US population by age and sex. Twelve years (2003–2014) of pooled diary data from the American Time Use Survey were used to calculate chronotype based on mid-point of sleep on weekends (MSFWe, n = 53,689). We observed a near-normal distribution overall and within each age group. The distribution’s mean value is systematically different with age, shifting later during adolescence, showing a peak in ‘lateness’ at ~19 years, and shifting earlier thereafter. Men are typically later chronotypes than women before 40, but earlier types after 40. The greatest differences are observed between 15 and 25 for both sexes, equaling more than 50% of the total chronotype difference across all age groups. The variability in chronotype decreases with age, but is generally higher in males than females. This is the first study to estimate the distribution and prevalence of individual chronotypes in the US population based on a large-scale, nationally representative sample. Our finding that adolescents are on average the latest chronotypes supports delaying school start times to benefit their sleep and circadian alignment. The generally wide range in chronotypes may provide opportunities for tailored work schedules by matching external and internal time, potentially decreasing long- and short-term health and safety risks.

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

“…We find a greater discordance to prior work with our cohort’s sleep midpoints, which typically vary from 3:00am to 4:30am in adults (Fischer et al 2017; Hashizaki et al 2015; Lucassen et al 2013; Rutters et al 2014). Our cohort’s sleep midpoints resemble those of morning chronotypes among obese adults (Lucassen et al 2013) and elderly individuals (Fischer et al 2017), which may speak to a morning preference in our older and obese cohort.…”

“…Gender and race did not explain social sleep lag, which may reflect broader cultural norms and chronotype shifts in younger individuals regardless of background. The average weekend sleep duration of 8.59 hours closely matches sleep durations from other studies based on self-reported data (Fischer et al 2017; Koopman et al 2017; Liu et al 2012; Rutters et al 2014), which commonly overestimates sleep duration compared with objective measures such as actigraphy (Arora et al 2013; Dietch et al 2017; Lauderdale et al 2006; Silva et al 2007). Well-established trends in both objective and subjective sleep analyses demonstrate whites sleep upwards of 45 minutes longer than blacks (Dietch et al 2017; Lauderdale et al 2006), and women sleep upwards of 30 minutes longer than men (Dietch et al 2017; Lauderdale et al 2006; Liu et al 2012; Urbanek et al 2017), which are all consistent with our findings.…”

“…These patterns are consistent with expected constraints of working-age adults and altered sleep requirements in the elderly and have been observed previously (Hashizaki et al 2015; Ohayon et al 2004; Silva et al 2007). Both sleep midpoint and sleep lag also vary as a function of age, which has been observed in adolescents and young adults (Hashizaki et al 2015; Fischer et al 2017; Koopman et al 2017; Rutters et al 2014; 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.…”

“…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 (Dietch et al 2017; Fischer et al 2017; Hashizaki et al 2015; Lauderdale et al 2006; Liu et al 2012; Ohayon et al 2004; Rutters et al 2014; Silva et al 2007; Urbanek et al 2017). Questionnaires such as the Morningness–Eveningness Questionnaire (Horne and Ostberg 1976) and Munich Chronotype Questionnaire (Roenneberg et al 2003) are common approaches to gauge an individual’s preferred schedule of activity and rest (i.e.…”

Relevance of a Mobile Internet Platform for Capturing Inter- and Intrasubject Variabilities in Circadian Coordination During Daily Routine: Pilot Study (Preprint)

“…Inter-subject variability in circadian phase has been suggested, based on chronotype questionnaires administered to large populations of presumably healthy subjects [15]. Inter-subject differences in both daily timing and 24-h pattern have also been shown in individual patients collecting up to 5 daily samples of salivary cortisol and/or melatonin determinations for up to 2 days [16][17][18].…”