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J Gerontol A Biol Sci Med Sci. 2018 Oct 29. doi: 10.1093/gerona/gly247. [Epub ahead of print]

Fractal complexity of daily physical activity patterns differs with age over the lifespan and is associated with mortality in older adults.

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School of Anthropology, University of Arizona, Tucson, AZ, USA.
Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
Departments of Psychology and Psychiatry, University of Arizona, Tucson, AZ, USA.
Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA.
Neuroscience Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA.
Physiological Sciences Graduate Interdisciplinary Program, University of Arizona, Tucson AZ, USA.
BIO5 Institute, University of Arizona, Tucson, AZ, USA.
Arizona Alzheimer's Consortium, Phoenix AZ, USA.



Accelerometers are included in a wide range of devices that monitor and track physical activity for health-related applications. However, the clinical utility of the information embedded in their rich time-series data has been greatly understudied and has yet to be fully realized. Here, we examine the potential for fractal complexity of actigraphy data to serve as a clinical biomarker for mortality risk.


We use Detrended Fluctuation Analysis (DFA) to analyze actigraphy data from the National Health and Nutrition Examination Survey (NHANES; n=11,694). The DFA method measures fractal complexity (signal self-affinity across timescales) as correlations between the amplitude of signal fluctuations in time-series data across a range of time-scales. The slope, α, relating the fluctuation amplitudes to the time-scales over which they were measured describes the complexity of the signal.


Fractal complexity of physical activity (α) decreased significantly with age (p=1.29E-6) and was lower in women compared with men (p=1.79E-4). Higher levels of moderate-to-vigorous physical activity in older adults and in women were associated with greater fractal complexity. In adults aged 50-79, lower fractal complexity of activity (α) was associated with greater mortality (HR=0.64; 95% CI=0.49-0.82) after adjusting for age, exercise engagement, chronic diseases, and other covariates associated with mortality.


Wearable accelerometers can provide a non-invasive biomarker of physiological aging and mortality risk after adjusting for other factors strongly associated with mortality. Thus, this fractal analysis of accelerometer signals provides a novel clinical application for wearable accelerometers, advancing efforts for remote monitoring of physiological health by clinicians.


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