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

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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.

Original languageEnglish (US)
Pages (from-to)1461-1467
Number of pages7
JournalThe journals of gerontology. Series A, Biological sciences and medical sciences
Volume74
Issue number9
DOIs
StatePublished - Aug 16 2019

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Fractals
Exercise
Mortality
Actigraphy
Nutrition Surveys
Biomarkers
Health
Physiologic Monitoring
Chronic Disease
Confidence Intervals
Equipment and Supplies

Keywords

  • Actigraphy
  • Detrended fluctuation analysis
  • Wearables

ASJC Scopus subject areas

  • Aging
  • Geriatrics and Gerontology

Cite this

@article{be11ca9b804b4bacbfe9723af11ce4d1,
title = "Fractal Complexity of Daily Physical Activity Patterns Differs With Age Over the Life Span and Is Associated With Mortality in Older Adults",
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 = "Actigraphy, Detrended fluctuation analysis, Wearables",
author = "Raichlen, {David A} and Klimentidis, {Yann C} and Chiu-Hsieh Hsu and Alexander, {Gene E}",
year = "2019",
month = "8",
day = "16",
doi = "10.1093/gerona/gly247",
language = "English (US)",
volume = "74",
pages = "1461--1467",
journal = "Journals of Gerontology - Series A Biological Sciences and Medical Sciences",
issn = "1079-5006",
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number = "9",

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TY - JOUR

T1 - Fractal Complexity of Daily Physical Activity Patterns Differs With Age Over the Life Span and Is Associated With Mortality in Older Adults

AU - Raichlen, David A

AU - Klimentidis, Yann C

AU - Hsu, Chiu-Hsieh

AU - Alexander, Gene E

PY - 2019/8/16

Y1 - 2019/8/16

N2 - 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.

AB - 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.

KW - Actigraphy

KW - Detrended fluctuation analysis

KW - Wearables

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U2 - 10.1093/gerona/gly247

DO - 10.1093/gerona/gly247

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C2 - 30371743

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VL - 74

SP - 1461

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JO - Journals of Gerontology - Series A Biological Sciences and Medical Sciences

JF - Journals of Gerontology - Series A Biological Sciences and Medical Sciences

SN - 1079-5006

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