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Sensors (Basel). 2020 Jan 19;20(2). pii: E549. doi: 10.3390/s20020549.

Characterizing Behavioral Activity Rhythms in Older Adults Using Actigraphy.

Author information

1
Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92697, USA.
2
Department of Psychology, Macquarie University, Sydney, NSW 2113, Australia.
3
Healthy Brain Ageing Program, Brain and Mind Centre, University of Sydney, Sydney, NSW 2006, Australia.
4
Department of Child, Family, and Population Health Nursing, University of Washington, Seattle, WA 98195, USA.
5
Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA.
6
Penn Behavioral Sleep Medicine Program, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA.

Abstract

Wrist actigraphy has been used to assess sleep in older adult populations for nearly half a century. Over the years, the continuous raw activity data derived from actigraphy has been used for the characterization of factors beyond sleep/wake such as physical activity patterns and circadian rhythms. Behavioral activity rhythms (BAR) are useful to describe individual daily behavioral patterns beyond sleep and wake, which represent important and meaningful clinical outcomes. This paper reviews common rhythmometric approaches and summarizes the available data from the use of these different approaches in older adult populations. We further consider a new approach developed in our laboratory designed to provide graphical characterization of BAR for the observed behavioral phenomenon of activity patterns across time. We illustrate the application of this new approach using actigraphy data collected from a well-characterized sample of older adults (age 60+) with osteoarthritis (OA) pain and insomnia. Generalized additive models (GAM) were implemented to fit smoothed nonlinear curves to log-transformed aggregated actigraphy-derived activity measurements. This approach demonstrated an overall strong model fit (R2 = 0.82, SD = 0.09) and was able to provide meaningful outcome measures allowing for graphical and parameterized characterization of the observed activity patterns within this sample.

KEYWORDS:

actigraphy; behavioral activity rhythms; circadian rhythms; older adults

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