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The difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach

BACKGROUND: Studies have seldom used Compositional Data Analysis (CoDA) to map the effects of sleep, sedentary behaviour, and physical activity on older adults’ cardio-metabolic profiles. This study therefore aimed to illustrate how sleep, sedentary behaviour, and physical activity profiles differ b...

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Autores principales: Ryan, Declan John, Wullems, Jorgen Antonin, Stebbings, Georgina Kate, Morse, Christopher Ian, Stewart, Claire Elizabeth, Onambele-Pearson, Gladys Leopoldine
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909533/
https://www.ncbi.nlm.nih.gov/pubmed/31890050
http://dx.doi.org/10.1186/s11556-019-0231-4
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author Ryan, Declan John
Wullems, Jorgen Antonin
Stebbings, Georgina Kate
Morse, Christopher Ian
Stewart, Claire Elizabeth
Onambele-Pearson, Gladys Leopoldine
author_facet Ryan, Declan John
Wullems, Jorgen Antonin
Stebbings, Georgina Kate
Morse, Christopher Ian
Stewart, Claire Elizabeth
Onambele-Pearson, Gladys Leopoldine
author_sort Ryan, Declan John
collection PubMed
description BACKGROUND: Studies have seldom used Compositional Data Analysis (CoDA) to map the effects of sleep, sedentary behaviour, and physical activity on older adults’ cardio-metabolic profiles. This study therefore aimed to illustrate how sleep, sedentary behaviour, and physical activity profiles differ between older adult groups (60–89 years), with ‘low’ compared to those with ‘high’ concentrations of endocrine cardio-metabolic disease risk markers, using CoDA. METHOD: Ninety-three participants (55% female) wore a thigh-mounted triaxial accelerometer for seven consecutive free-living days. Accelerometer estimates of daily average hours of engagement in sedentary behaviour (SB), standing, light-intensity physical activity (LIPA), sporadic moderate-vigorous physical activity (sMVPA, accumulated with bouts between 1 and 10 min), 10-min moderate-vigorous physical activity ((10)MVPA, accumulated with bouts ≥10 min), in addition to self-reported sleeping hours were reported. Fasted whole blood concentrations of total cholesterol, triglyceride, glucose, and glycated haemoglobin, and serum lipoprotein lipase (LPL), interleukin-6 (IL-6), and procollagen III N-terminal propeptide were determined. RESULTS: Triglyceride concentration appeared to be highly dependent on (10)MVPA engagement as the ‘low’ and ‘high’ concentration groups engaged in 48% more and 32% less (10)MVPA, respectively, relative to the geometric mean of the entire study sample. Time-use composition of the ‘low’ LPL group’s engagement in (10)MVPA was 26% less, while the ‘high’ LPL group was 7.9% more, than the entire study sample. Time-use composition of the ‘high’ glucose and glycated haemoglobin groups appeared to be similar as both engaged in more Sleep and SB, and less (10)MVPA compared to the study sample. Participants with a ‘low’ IL-6 concentration engaged in 4.8% more Sleep and 2.7% less (10)MVPA than the entire study sample. Time-use composition of the Total Cholesterol groups was mixed with the ‘low’ concentration group engaging in more Standing and (10)MVPA but less Sleep, SB, LIPA, and sMVPA than the entire study sample. CONCLUSION: Older adults should aim to increase 10MVPA engagement to improve lipid profile and decrease SB engagement to improve glucose profile.
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spelling pubmed-69095332019-12-30 The difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach Ryan, Declan John Wullems, Jorgen Antonin Stebbings, Georgina Kate Morse, Christopher Ian Stewart, Claire Elizabeth Onambele-Pearson, Gladys Leopoldine Eur Rev Aging Phys Act Research Article BACKGROUND: Studies have seldom used Compositional Data Analysis (CoDA) to map the effects of sleep, sedentary behaviour, and physical activity on older adults’ cardio-metabolic profiles. This study therefore aimed to illustrate how sleep, sedentary behaviour, and physical activity profiles differ between older adult groups (60–89 years), with ‘low’ compared to those with ‘high’ concentrations of endocrine cardio-metabolic disease risk markers, using CoDA. METHOD: Ninety-three participants (55% female) wore a thigh-mounted triaxial accelerometer for seven consecutive free-living days. Accelerometer estimates of daily average hours of engagement in sedentary behaviour (SB), standing, light-intensity physical activity (LIPA), sporadic moderate-vigorous physical activity (sMVPA, accumulated with bouts between 1 and 10 min), 10-min moderate-vigorous physical activity ((10)MVPA, accumulated with bouts ≥10 min), in addition to self-reported sleeping hours were reported. Fasted whole blood concentrations of total cholesterol, triglyceride, glucose, and glycated haemoglobin, and serum lipoprotein lipase (LPL), interleukin-6 (IL-6), and procollagen III N-terminal propeptide were determined. RESULTS: Triglyceride concentration appeared to be highly dependent on (10)MVPA engagement as the ‘low’ and ‘high’ concentration groups engaged in 48% more and 32% less (10)MVPA, respectively, relative to the geometric mean of the entire study sample. Time-use composition of the ‘low’ LPL group’s engagement in (10)MVPA was 26% less, while the ‘high’ LPL group was 7.9% more, than the entire study sample. Time-use composition of the ‘high’ glucose and glycated haemoglobin groups appeared to be similar as both engaged in more Sleep and SB, and less (10)MVPA compared to the study sample. Participants with a ‘low’ IL-6 concentration engaged in 4.8% more Sleep and 2.7% less (10)MVPA than the entire study sample. Time-use composition of the Total Cholesterol groups was mixed with the ‘low’ concentration group engaging in more Standing and (10)MVPA but less Sleep, SB, LIPA, and sMVPA than the entire study sample. CONCLUSION: Older adults should aim to increase 10MVPA engagement to improve lipid profile and decrease SB engagement to improve glucose profile. BioMed Central 2019-12-12 /pmc/articles/PMC6909533/ /pubmed/31890050 http://dx.doi.org/10.1186/s11556-019-0231-4 Text en © The Author(s) 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Ryan, Declan John
Wullems, Jorgen Antonin
Stebbings, Georgina Kate
Morse, Christopher Ian
Stewart, Claire Elizabeth
Onambele-Pearson, Gladys Leopoldine
The difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach
title The difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach
title_full The difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach
title_fullStr The difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach
title_full_unstemmed The difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach
title_short The difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach
title_sort difference in sleep, sedentary behaviour, and physical activity between older adults with ‘healthy’ and ‘unhealthy’ cardiometabolic profiles: a cross-sectional compositional data analysis approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6909533/
https://www.ncbi.nlm.nih.gov/pubmed/31890050
http://dx.doi.org/10.1186/s11556-019-0231-4
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