Demographic characteristics associated with circadian rest-activity rhythm patterns: a cross-sectional study

Abstract

Background Rest-activity rhythm (RAR), a manifestation of circadian rhythms, has been associated with morbidity and mortality risk. However, RAR patterns in the general population and specifically the role of demographic characteristics in RAR pattern have not been comprehensively assessed. Therefore, we aimed to describe RAR patterns among non-institutionalized US adults and age, sex, and race/ethnicity variation using accelerometry data from a nationally representative population. Methods This cross-sectional study was conducted using the US National Health and Nutrition Examination Survey (NHANES) 2011–2014. Participants aged ≥20 years who were enrolled in the physical activity monitoring examination and had at least four 24-h periods of valid wrist accelerometer data were included in the present analysis. 24-h RAR metrics were generated using both extended cosinor model (amplitude, mesor, acrophase and pseudo-F statistic) and nonparametric methods (interdaily stability [IS] and intradaily variability [IV]). Multivariable linear regression was used to assess the association between RAR and age, sex, and race/ethnicity. Results Eight thousand two hundred participants (mean [SE] age, 49.1 [0.5] years) were included, of whom 52.2% were women and 67.3% Whites. Women had higher RAR amplitude and mesor, and also more robust (pseudo-F statistic), more stable (higher IS) and less fragmented (lower IV) RAR (all Ptrend < 0.001) than men. Compared with younger adults (20–39 years), older adults (≥ 60 years) exhibited reduced RAR amplitude and mesor, but more stable and less fragmented RAR, and also reached their peak activity earlier (advanced acrophase) (all Ptrend < 0.001). Relative to other racial/ethnic groups, Hispanics had the highest amplitude and mesor level, and most stable (highest IS) and least fragmented (lowest IV) RAR pattern (Ptrend < 0.001). Conversely, non-Hispanic blacks had the lowest peak activity level (lowest amplitude) and least stable (lowest IS) RAR pattern (all Ptrend < 0.001). Conclusions In the general adult population, RAR patterns vary significantly according to sex, age and race/ethnicity. These results may reflect demographic-dependent differences in intrinsic circadian rhythms and may have important implications for understanding racial, ethnic, sex and other disparities in morbidity and mortality risk.

Rest-Activity Rhythm Is Associated With Obesity Phenotypes: A Cross-Sectional Analysis

“…In a previous study, we found that RAR patterns vary by age and sex ( 28 ). However, our age-stratified results showed that younger and older adults with disrupted RAR are at similar risk of having excess adiposity, implying that age was not an effect modifier of the association with obesity.…”

“…Detailed information regarding generation of RAR metrics has been reported elsewhere ( 28 ). In brief, participants were instructed to wear an accelerometer (ActiGraph GT3X+, Pensacola, FL) on their non-dominant wrist for seven consecutive 24-hour periods.…”

Rest-activity circadian rhythm and impaired glucose tolerance in adults: an analysis of NHANES 2011–2014

“…Wrist accelerometers were incorporated in the 2011–2014 NHANES study cycle and this is the first time that 24-hour objective actigraphy data are available on a nationally representative sample of US residents. 22 NHANES used a multistage probability sampling design to produce a weighted, representative sample of the US population. 23 Our sample included adults ≥20 years old who had at least 4 days of validated accelerometer recordings and valid oral glucose tolerance test (OGTT) data from NHANES 2011–2014 cycles (data set 24 ).…”

Executive Performance Is Associated With Rest-Activity Rhythm in Nurses Working Rotating Shifts

“…These changes are known as biological rhythms and are induced by signals from internal molecular mechanisms. In human beings, sleep-wake activity follows a diurnal pattern which is called the rest–activity rhythm (RAR) ( Li et al, 2021 ). In recent years, a variety of methods recording several days of continuous accelerometer data could be used to detect RAR features ( Smagula et al, 2019 ).…”