Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/4/1545 |
| _version_ | 1797395593827450880 |
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| author | Julia Wrobel John Muschelli Andrew Leroux |
| author_facet | Julia Wrobel John Muschelli Andrew Leroux |
| author_sort | Julia Wrobel |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T00:36:45Z |
| format | Article |
| id | doaj.art-b1f0e05b99a8411e996aec3eb12cc89e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T00:36:45Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b1f0e05b99a8411e996aec3eb12cc89e2023-12-11T18:05:35ZengMDPI AGSensors1424-82202021-02-01214154510.3390/s21041545Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank StudyJulia Wrobel0John Muschelli1Andrew Leroux2Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USADepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21218, USADepartment of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USAThe 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.https://www.mdpi.com/1424-8220/21/4/1545accelerometersagingUK Biobankfunctional regressioncurve registration |
| spellingShingle | Julia Wrobel John Muschelli Andrew Leroux Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study Sensors accelerometers aging UK Biobank functional regression curve registration |
| title | Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study |
| title_full | Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study |
| title_fullStr | Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study |
| title_full_unstemmed | Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study |
| title_short | Diurnal Physical Activity Patterns across Ages in a Large UK Based Cohort: The UK Biobank Study |
| title_sort | diurnal physical activity patterns across ages in a large uk based cohort the uk biobank study |
| topic | accelerometers aging UK Biobank functional regression curve registration |
| url | https://www.mdpi.com/1424-8220/21/4/1545 |
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