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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.

Author information

1
School of Anthropology, University of Arizona, Tucson, AZ, USA.
2
Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona, Tucson, AZ, USA.
3
Departments of Psychology and Psychiatry, University of Arizona, Tucson, AZ, USA.
4
Evelyn F. McKnight Brain Institute, University of Arizona, Tucson, AZ, USA.
5
Neuroscience Graduate Interdisciplinary Program, University of Arizona, Tucson, AZ, USA.
6
Physiological Sciences Graduate Interdisciplinary Program, University of Arizona, Tucson AZ, USA.
7
BIO5 Institute, University of Arizona, Tucson, AZ, USA.
8
Arizona Alzheimer's Consortium, Phoenix AZ, USA.

Abstract

Background:

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.

Methods:

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.

Results:

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.

Conclusions:

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.

PMID:
30371743
DOI:
10.1093/gerona/gly247

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