Next Article in Journal
The Optical Signal-to-Crosstalk Ratio for the MBA(N, e, g) Switching Fabric
Next Article in Special Issue
Profiles of Accelerometry-Derived Physical Activity are Related to Perceived Physical Fatigability in Older Adults
Previous Article in Journal
Invariant Image Representation Using Novel Fractional-Order Polar Harmonic Fourier Moments
Previous Article in Special Issue
Quantifying the Varying Predictive Value of Physical Activity Measures Obtained from Wearable Accelerometers on All-Cause Mortality over Short to Medium Time Horizons in NHANES 2003–2006
Open AccessArticle

Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study

1
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
2
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Lars Donath
Sensors 2021, 21(4), 1545; https://doi.org/10.3390/s21041545
Received: 4 January 2021 / Revised: 10 February 2021 / Accepted: 18 February 2021 / Published: 23 February 2021
(This article belongs to the Special Issue Wearable Devices: Applications in Older Adults)
The ability of individuals to engage in physical activity is a critical component of overall health and quality of life. However, there is a natural decline in physical activity associated with the aging process. Establishing normative trends of physical activity in aging populations is essential to developing public health guidelines and informing clinical perspectives regarding individuals’ levels of physical activity. Beyond overall quantity of physical activity, patterns regarding the timing of activity provide additional insights into latent health status. Wearable accelerometers, paired with statistical methods from functional data analysis, provide the means to estimate diurnal patterns in physical activity. To date, these methods have been only applied to study aging trends in populations based in the United States. Here, we apply curve registration and functional regression to 24 h activity profiles for 88,793 men (N = 39,255) and women (N = 49,538) ages 42–78 from the UK Biobank accelerometer study to understand how physical activity patterns vary across ages and by gender. Our analysis finds that daily patterns in both the volume of physical activity and probability of being active change with age, and that there are marked gender differences in these trends. This work represents the largest-ever population analyzed using tools of this kind, and suggest that aging trends in physical activity are reproducible in different populations across countries. View Full-Text
Keywords: accelerometers; aging; UK Biobank; functional regression; curve registration accelerometers; aging; UK Biobank; functional regression; curve registration
Show Figures

Figure 1

MDPI and ACS Style

Wrobel, J.; Muschelli, J.; Leroux, A. Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study. Sensors 2021, 21, 1545. https://doi.org/10.3390/s21041545

AMA Style

Wrobel J, Muschelli J, Leroux A. Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study. Sensors. 2021; 21(4):1545. https://doi.org/10.3390/s21041545

Chicago/Turabian Style

Wrobel, Julia; Muschelli, John; Leroux, Andrew. 2021. "Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study" Sensors 21, no. 4: 1545. https://doi.org/10.3390/s21041545

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop