Variation in actigraphy-estimated rest-activity patterns by demographic factors. (14th September 2017) - Mitchell, Jonathan A; Quante, Mirja; Godbole, Suneeta; James, Peter; Hipp, J. Aaron; Marinac, Catherine R; Mariani, Sara; Cespedes Feliciano, Elizabeth M.; Glanz, Karen; Laden, Francine; Wang, Rui; Weng, Jia; et al.
ABSTRACT: Rest-activity patterns provide an indication of circadian rhythmicity in the free-living setting. We aimed to describe the distributions of rest-activity patterns in a sample of adults and children across demographic variables. A sample of adults ( N  = 590) and children ( N  = 58) wore an actigraph on their nondominant wrist for 7 days and nights. We generated rest-activity patterns from cosinor analysis (MESOR, acrophase and magnitude) and nonparametric circadian rhythm analysis (IS: interdaily stability; IV: intradaily variability; L5: least active 5-hour period; M10: most active 10-hour period; and RA: relative amplitude). Demographic variables included age, sex, race, education, marital status, and income. Linear mixed-effects models were used to test for demographic differences in rest-activity patterns. Adolescents, compared to younger children, had (1) later M10 midpoints ( β  = 1.12 hours [95% CI: 0.43, 1.18] and lower M10 activity levels; (2) later L5 midpoints ( β  = 1.6 hours [95% CI: 0.9, 2.3]) and lower L5 activity levels; (3) less regular rest-activity patterns (lower IS and higher IV); and 4) lower magnitudes ( β  = −0.95 [95% CI: −1.28, −0.63]) and relative amplitudes ( β  = −0.1 [95% CI: −0.14, −0.06]). Mid-to-older adults, compared to younger adults (aged 18–29 years), had (1) earlier M10 midpoints ( β  = −1.0 hours [95% CI: −1.6, −0.4]; (2) earlier L5 midpoints ( β  = −0.7 hours [95% CI: −1.2, −0.2]); and (3) more regular rest-activity patterns (higher IS and lower IV). The magnitudes and relative amplitudes were similar across the adult age categories. Sex, race and education level rest-activity differences were also observed. Rest-activity patterns vary across the lifespan, and differ by race, sex and education. Understanding population variation in these patterns provides a foundation for further elucidating the health implications of rest-activity patterns across the lifespan.