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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1545
Author(s):  
Julia Wrobel ◽  
John Muschelli ◽  
Andrew Leroux
Keyword(s):  
Physical Activity ◽  
Activity Patterns ◽  
The United States ◽  
Functional Regression ◽  
Uk Biobank ◽  
Physical Activity Patterns ◽  
Aging Populations ◽  
Different Populations ◽  
The Uk ◽  
Wearable Accelerometers

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.

Contrasting physical activity patterns in children and adolescents living in differing environments in the UK

2011 ◽  
Vol 39 (7) ◽  
pp. 696-703 ◽  
Author(s):  
A.A. Ogunleye ◽  
C. Voss ◽  
J.L. Barton ◽  
J.N. Pretty ◽  
G.R.H. Sandercock
Keyword(s):  
Physical Activity ◽  
Children And Adolescents ◽  
Activity Patterns ◽  
Physical Activity Patterns ◽  
The Uk

scholarly journals Physical activity patterns of ethnic children from low socio-economic environments within the UK

2014 ◽  
Vol 33 (3) ◽  
pp. 232-242 ◽  
Author(s):  
Emma Lisa Jane Eyre ◽  
Michael Joseph Duncan ◽  
Samantha Louise Birch ◽  
Valerie Cox ◽  
Matthew Blackett
Keyword(s):  
Physical Activity ◽  
Activity Patterns ◽  
Physical Activity Patterns ◽  
The Uk

scholarly journals Clustering Accelerometer Activity Patterns from the UK Biobank Cohort

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8220
Author(s):  
Stephen Clark ◽  
Nik Lomax ◽  
Michelle Morris ◽  
Francesca Pontin ◽  
Mark Birkin
Keyword(s):  
Physical Activity ◽  
Circadian Rhythm ◽  
Activity Patterns ◽  
Activity Levels ◽  
Uk Biobank ◽  
Activity Profile ◽  
Data Set ◽  
Robust Clustering ◽  
Extensive Data ◽  
The Uk

Many researchers are beginning to adopt the use of wrist-worn accelerometers to objectively measure personal activity levels. Data from these devices are often used to summarise such activity in terms of averages, variances, exceedances, and patterns within a profile. In this study, we report the development of a clustering utilising the whole activity profile. This was achieved using the robust clustering technique of k-medoids applied to an extensive data set of over 90,000 activity profiles, collected as part of the UK Biobank study. We identified nine distinct activity profiles in these data, which captured both the pattern of activity throughout a week and the intensity of the activity: “Active 9 to 5”, “Active”, “Morning Movers”, “Get up and Active”, “Live for the Weekend”, “Moderates”, “Leisurely 9 to 5”, “Sedate” and “Inactive”. These patterns are differentiated by sociodemographic, socioeconomic, and health and circadian rhythm data collected by UK Biobank. The utility of these findings are that they sit alongside existing summary measures of physical activity to provide a way to typify distinct activity patterns that may help to explain other health and morbidity outcomes, e.g., BMI or COVID-19. This research will be returned to the UK Biobank for other researchers to use.

Physical activity patterns in chronic hemodialysis patients: Comparison of dialysis and nondialysis days

2005 ◽  
Vol 15 (2) ◽  
pp. 217-224 ◽  
Author(s):  
Karen M. Majchrzak ◽  
Lara B. Pupim ◽  
Kong Chen ◽  
Cathi J. Martin ◽  
Sheila Gaffney ◽  
...  
Keyword(s):  
Physical Activity ◽  
Activity Patterns ◽  
Hemodialysis Patients ◽  
Chronic Hemodialysis ◽  
Physical Activity Patterns

Physical activity patterns among walkers and compliance with public health recommendations

2002 ◽  
Vol 34 (8) ◽  
pp. 1255-1261 ◽  
Author(s):  
ANN P. RAFFERTY ◽  
MATHEW J. REEVES ◽  
HARRY B. MCGEE ◽  
JAMES M. PIVARNIK
Keyword(s):  
Public Health ◽  
Physical Activity ◽  
Activity Patterns ◽  
Physical Activity Patterns

scholarly journals Physical Activity Patterns After Retirement: The REGARDS Study

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 528-529
Author(s):  
Eric Shiroma ◽  
J David Rhodes ◽  
Aleena Bennet ◽  
Monika M Safford ◽  
Leslie MacDonald ◽  
...  
Keyword(s):  
Physical Activity ◽  
Light Intensity ◽  
Racial Differences ◽  
Activity Patterns ◽  
Accelerometer Data ◽  
Tv Viewing ◽  
Major Life Events ◽  
Major Life ◽  
Physical Activity Patterns ◽  
Regards Study

Abstract Major life events, such as retirement, may lead to dramatic shifts in physical activity (PA) patterns. However, there are limited empirical data quantifying the magnitude of these changes. Our aims were to objectively measure PA before and after retirement and to describe changes in participation in various types of PA. Participants were employed black and white men and women enrolled in REGARDS (REasons for Geographic and Racial Differences in Stroke), a national prospective cohort study (n=581, mean age 64 years, 25% black, 51% women). Participants met inclusion criteria if they retired between their first and second accelerometer wearing (2009-2013 and 2017-2018, respectively) and had valid accelerometer data (>4 days with >10 hours/day pre- and post-retirement). Accelerometer-based PA was categorized into average minutes per day spent in sedentary, light-intensity, and moderate-to-vigorous PA. Participants reported changes (less, same, more) in 12 types of PA. After retirement, participants decreased both sedentary time (by 36.3 minutes/day) and moderate-to-vigorous PA (by 5.6 minutes/day). Conversely, there was an increase in light-intensity PA (+18.1 minutes/day) after retirement. Participants reported changes in their participation level in various PA activities. For example, 41% reported an increased amount of TV viewing, 42% reported less walking, and 31% reported increased participation in volunteer activities. Findings indicate that retirement coincides with a change in the time spent in each intensity category and the time spent across a range of activity types. Further research is warranted to examine how these changes in physical activity patterns influence post-retirement health status.

scholarly journals Objective measurements of daily physical activity patterns and sedentary behaviour in older adults: Age, Gene/Environment Susceptibility-Reykjavik Study

2012 ◽  
Vol 42 (2) ◽  
pp. 222-229 ◽  
Author(s):  
Nanna Yr Arnardottir ◽  
Annemarie Koster ◽  
Dane R. Van Domelen ◽  
Robert J. Brychta ◽  
Paolo Caserotti ◽  
...  
Keyword(s):  
Physical Activity ◽  
Older Adults ◽  
Sedentary Behaviour ◽  
Activity Patterns ◽  
Daily Physical Activity ◽  
Physical Activity Patterns ◽  
Objective Measurements ◽  
Gene Environment

scholarly journals Physical Activity Patterns in the National Weight Control Registry

Obesity ◽  
2008 ◽  
Vol 16 (1) ◽  
pp. 153-161 ◽  
Author(s):  
Victoria A. Catenacci ◽  
Lorraine G. Ogden ◽  
Jennifer Stuht ◽  
Suzanne Phelan ◽  
Rena R. Wing ◽  
...  
Keyword(s):  
Physical Activity ◽  
Weight Control ◽  
Activity Patterns ◽  
Physical Activity Patterns
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