Fractal Complexity of Daily Physical Activity Patterns Differs With Age Over the Life Span and Is Associated With Mortality in Older Adults

Journals of Gerontology: Medical Sciences cite as: J Gerontol A Biol Sci Med Sci, 2019, Vol. 74, No. 9, 1461–1467 doi:10.1093/gerona/gly247 Advance Access publication October 29, 2018 Research Article David A. Raichlen, PhD,1,* Yann C. Klimentidis, PhD,2,3 Chiu-Hsieh Hsu, PhD,2 and Gene E. Alexander, PhD3,4,5,6,7,8 School of Anthropology, 2Department of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, 3BIO5 Institute, University of Arizona, Tucson.4Departments of Psychology and Psychiatry, 5Evelyn F. McKnight Brain Institute, 6Neuroscience Graduate Interdisciplinary Program, 7Physiological Sciences Graduate Interdisciplinary Program, and 8Arizona Alzheimer’s Consortium, Phoenix. 1 *Address correspondence to: David A. Raichlen, PhD, School of Anthropology, University of Arizona, 1009 E. South Campus Dr., Tucson, AZ 85721. E-mail: Received: March 5, 2018; Editorial Decision Date: October 15, 2018 Decision Editor: Anne Newman, MD, MPH 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 time-scales) 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 years, lower fractal complexity of activity (α) was associated with greater mortality (hazard ratio = 0.64; 95% confidence interval = 0.49–0.82) after adjusting for age, exercise engagement, chronic diseases, and other covariates associated with mortality. Conclusions: Wearable accelerometers can provide a noninvasive 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. Keywords: Detrended fluctuation analysis, Actigraphy, Wearables The growing use of wearable physical activity monitors holds great promise for clinical medicine (1). Through individual behavioral assessment, these devices may improve early disease detection and provide a powerful aid to precision medicine initiatives (2–4). However, because activity monitors are often used to simply track behavior, their utility as diagnostic tools remains unclear (2,3). While monitoring exercise participation may facilitate behavioral interventions, accelerometers in these devices also contain rich time-series data sets measuring overall patterns of movement that remain highly understudied. Here, we analyze wearable accelerometry data from a large, nationally representative U.S. sample of individuals ranging from 6 to over 85 years of age using a novel fractal method, and show that fluctuating patterns of daily activity can provide a physiologically relevant clinical endpoint that is associated with aging over the life span and is associated with mortality risk in communitydwelling older adults. © The Author(s) 2018. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: . 1461 Fractal Complexity of Daily Physical Activity Patterns Differs With Age Over the Life Span and Is Associated With Mortality in Older Adults Journals of Gerontology: MEDICAL SCIENCES, 2019, Vol. 74, No. 9 1462 Acceleration Intensity Acceleration Intensity 10000 5000 5000 0 0 0 Fluctuation Function: f(t) 10000 25 50 time (hrs) 75 100 0 20 40 time (hrs) 60 10000 1000 10 100 Time Scale: t (minutes) Figure 1. Example of detrended fluctuation analysis (DFA) in two subjects. (A and B) Accelerometer intensities for two subjects. (C) DFA analysis for subjects shown in A and B. Points show the change in fluctuation function across the range of time-scales used in the DFA analysis (from 10 minutes to 7 hours). Blue values show a subject with healthy fractal complexity (from A), indicated by a slope of the line relating F(t) to t of 1.0. Red values show a subject with reduced signal complexity (from B), indicated by a slope of 0.50. While fractal analyses of activity may provide a promising avenue for the development of noninvasive biomarkers of disease, to date, human studies have only tracked small samples of individuals over a limited age range, and have not examined mortality as an endpoint. Using accelerometer data from over 11,600 participants in the National Health and Nutrition Examination Survey (NHANES), we sought to determine whether fractal complexity of physical activity patterns differs with age (acting as a biomarker of physiological senescence), whether these patterns are associated with exercise engagement, and whether fractal complexity is associated with mortality risk among older adults. Materials and Methods Actigraphy data were collected from the publicly available NHANES data set (9). NHANES is a large, stratified, multistage probability sample that is nationally representative of the community-dwelling U.S. population, spanning the life span from 6 to over 85 years of age (9). In NHANES, individuals older than 6 (mean age ± SD = 35.5 ± 23.5; see Supplementary Table 1) were asked to wear an ActiGraph AM-7164 accelerometer (ActiGraph, LLC, Fort Walton Beach, FL) on a belt around their waist for 7 days, except when bathing or sleeping (21). Accelerometer values are available for the 2003–2004 and 2005–2006 NHANES waves. We excluded subjects with less than 2 days of valid data (defined as data that were calibrated, returned with no reliability flags, and contained between 10 and 20 hours of valid wear time per day). Previous work has shown that 2 days of accelerometer data accurately capture physical activity levels and patterns in adults (22–24), and recent studies examining PA (25), and the association of accelerometer derived PA and mortality in the NHANES data set (26,27) have included participants with at least 1 day of valid wear time. We chose 2 days of wear time here as (...truncated)