Cormac Powell1,2, Leonard D Browne3,4, Brian P Carson5,6,4, Kieran P Dowd7, Ivan J Perry8, Patricia M Kearney8, Janas M Harrington8, Alan E Donnelly9,10,11. 1. Performance Department, Swim Ireland, Sport HQ, Dublin, Ireland. cormacpowell@swimireland.ie. 2. Physical Activity for Health Research Cluster, University of Limerick, Limerick, Ireland. cormacpowell@swimireland.ie. 3. Graduate Entry Medical School, University of Limerick, Limerick, Ireland. 4. Health Research Institute, University of Limerick, Limerick, Ireland. 5. Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland. 6. Physical Activity for Health Research Cluster, University of Limerick, Limerick, Ireland. 7. Department of Sport and Health, Athlone Institute of Technology, Westmeath, Ireland. 8. HRB Centre for Health and Diet Research School of Public Health, University College Cork, Cork, Ireland. 9. Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland. alan.donnelly@ul.ie. 10. Physical Activity for Health Research Cluster, University of Limerick, Limerick, Ireland. alan.donnelly@ul.ie. 11. Health Research Institute, University of Limerick, Limerick, Ireland. alan.donnelly@ul.ie.
Abstract
BACKGROUND: All physical activity (PA) behaviours undertaken over the day, including sleep, sedentary time, standing time, light-intensity PA (LIPA) and moderate-to-vigorous PA (MVPA) have the potential to influence cardiometabolic health. Since these behaviours are mutually exclusive, standard statistical approaches are unable to account for the impact on time spent in other behaviours. OBJECTIVE: By employing a compositional data analysis (CoDA) approach, this study examined the associations of objectively measured time spent in sleep, sedentary time, standing time, LIPA and MVPA over a 24-h day on markers of cardiometabolic health in older adults. METHODS: Participants (n =366; 64.6 years [5.3]; 46% female) from the Mitchelstown Cohort Rescreen Study provided measures of body composition, blood lipid and markers of glucose control. An activPAL3 Micro was used to obtain objective measures of sleep, sedentary time, standing time, LIPA and MVPA, using a 7-day continuous wear protocol. Regression analysis, using geometric means derived from CoDA (based on isometric log-ratio transformed data), was used to examine the relationship between the aforementioned behaviours and markers of cardiometabolic health. RESULTS: Standing time and LIPA showed diverging associations with markers of body composition. Body mass index (BMI), body mass and fat mass were negatively associated with LIPA (all p <0.05) and positively associated with standing time (all p <0.05). Sedentary time was also associated with higher BMI (p <0.05). No associations between blood markers and any PA behaviours were observed, except for triglycerides, which were negatively associated with standing time (p < 0.05). Reallocating 30 min from sleep, sedentary time or standing time, to LIPA, was associated with significant decreases in BMI, body fat and fat mass. CONCLUSION: This is the first study to employ CoDA in older adults that has accounted for sleep, sedentary time, standing time, LIPA and MVPA in a 24-h cycle. The findings support engagement in LIPA to improve body composition in older adults. Increased standing time was associated with higher levels of adiposity, with increased LIPA associated with reduced adiposity; therefore, these findings indicate that replacing standing time with LIPA is a strategy to lower adiposity.
BACKGROUND: All physical activity (PA) behaviours undertaken over the day, including sleep, sedentary time, standing time, light-intensity PA (LIPA) and moderate-to-vigorous PA (MVPA) have the potential to influence cardiometabolic health. Since these behaviours are mutually exclusive, standard statistical approaches are unable to account for the impact on time spent in other behaviours. OBJECTIVE: By employing a compositional data analysis (CoDA) approach, this study examined the associations of objectively measured time spent in sleep, sedentary time, standing time, LIPA and MVPA over a 24-h day on markers of cardiometabolic health in older adults. METHODS:Participants (n =366; 64.6 years [5.3]; 46% female) from the Mitchelstown Cohort Rescreen Study provided measures of body composition, blood lipid and markers of glucose control. An activPAL3 Micro was used to obtain objective measures of sleep, sedentary time, standing time, LIPA and MVPA, using a 7-day continuous wear protocol. Regression analysis, using geometric means derived from CoDA (based on isometric log-ratio transformed data), was used to examine the relationship between the aforementioned behaviours and markers of cardiometabolic health. RESULTS: Standing time and LIPA showed diverging associations with markers of body composition. Body mass index (BMI), body mass and fat mass were negatively associated with LIPA (all p <0.05) and positively associated with standing time (all p <0.05). Sedentary time was also associated with higher BMI (p <0.05). No associations between blood markers and any PA behaviours were observed, except for triglycerides, which were negatively associated with standing time (p < 0.05). Reallocating 30 min from sleep, sedentary time or standing time, to LIPA, was associated with significant decreases in BMI, body fat and fat mass. CONCLUSION: This is the first study to employ CoDA in older adults that has accounted for sleep, sedentary time, standing time, LIPA and MVPA in a 24-h cycle. The findings support engagement in LIPA to improve body composition in older adults. Increased standing time was associated with higher levels of adiposity, with increased LIPA associated with reduced adiposity; therefore, these findings indicate that replacing standing time with LIPA is a strategy to lower adiposity.
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