Variation in actigraphy-estimated rest-activity patterns by demographic factors

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.

Keywords:

• actigraphy
• epidemiology
• rest-activity patterns
• demographics

Declaration of Interest

The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.

Funding

This work was supported by the NCI Centers for Transdisciplinary Research on Energetics and Cancer (TREC) (U01 CA116850, U54 CA155496, U54 CA155626, U54 CA155435, U54 CA155850). The Nurses’ Health Study II is supported by National Cancer Institute grant UM1 CA176726. The iWatch study was funded by National Institutes of Health (NIH) grants (1R01CA164993). Jonathan Mitchell was supported by NIH grant K01 HL123612 (NHLBI). Catherine Marinac was supported by the National Cancer Institute under award number 1F31 CA183125. Peter James was supported by National Cancer Institute grant K99 CA201542. Mirja Quante was supported by a scholarship from the Tuebinger Program for the Advancement of Women in Science. Sara Mariani was supported by NIH grant R24HL114473 (NHLBI).

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Additional information

Funding

This work was supported by the NCI Centers for Transdisciplinary Research on Energetics and Cancer (TREC) (U01 CA116850, U54 CA155496, U54 CA155626, U54 CA155435, U54 CA155850). The Nurses’ Health Study II is supported by National Cancer Institute grant UM1 CA176726. The iWatch study was funded by National Institutes of Health (NIH) grants (1R01CA164993). Jonathan Mitchell was supported by NIH grant K01 HL123612 (NHLBI). Catherine Marinac was supported by the National Cancer Institute under award number 1F31 CA183125. Peter James was supported by National Cancer Institute grant K99 CA201542. Mirja Quante was supported by a scholarship from the Tuebinger Program for the Advancement of Women in Science. Sara Mariani was supported by NIH grant R24HL114473 (NHLBI).